Three companies shipped their best AI model within the same 14-week stretch of early 2026, and each one leads a different scoreboard. Google DeepMind moved first with Gemini 3.1 Pro on February 19. OpenAI followed with GPT-5.5 on April 23. Anthropic closed the window on May 28 with Claude Opus 4.8, which became the first model to cross 60 on the Artificial Analysis Intelligence Index, landing at 61.4 according to Artificial Analysis.
That timing is not a coincidence. Anthropic, OpenAI, and Google are locked into a release cadence where each lab ships a flagship update roughly every six to ten weeks, and the gap between “best available model” and “second best” now closes in days rather than months. For anyone choosing between ChatGPT, Claude, and Gemini for coding, research, or enterprise deployment in June 2026, the practical differences come down to price per token, context window, and which benchmark actually predicts real work. This comparison covers what each model costs, how each one scores on the benchmarks that matter, and which one fits which job.
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Claude Opus 4.8 vs GPT-5.5 vs Gemini 3.1 Pro: The Short Answer
Claude Opus 4.8 leads on agentic coding and sustained multi-step tasks. GPT-5.5 leads on terminal and developer-tool workflows through Codex. Gemini 3.1 Pro leads on multimodal input and cost efficiency at scale. Anthropic’s model posted 88.6% on SWE-bench Verified, a benchmark that measures whether a model can resolve real GitHub issues end to end, and it holds the top spot on the Artificial Analysis Intelligence Index. OpenAI’s GPT-5.5 leads Terminal-Bench 2.0 at 82.7%, a benchmark built around command-line and agentic tool use. Google’s Gemini 3.1 Pro leads GPQA Diamond graduate-level science reasoning at 94.1% and the cited pricing claim is not confirmed by the available evidence.
None of the three wins every category, and the right pick depends on what actually gets automated. A team running long agentic coding sessions gets more mileage out of Claude Opus 4.8’s efficiency gains (Anthropic says it uses 35% fewer output tokens than Opus 4.7 for comparable tasks). A team already built on OpenAI’s Codex and Atlas browser stack gets tighter integration from GPT-5.5. A team processing video, audio, or long PDFs at volume saves the most money running Gemini 3.1 Pro, which is also the only one of the three built natively for 1,000-page document analysis and hour-long video ingestion.
Full Specs Comparison: Claude Opus 4.8, GPT-5.5, and Gemini 3.1 Pro
Here is how the three flagships stack up on paper, pulled from each vendor’s own documentation and release notes. Pricing and context figures reflect standard API access as of June 2026.
| Spec | Claude Opus 4.8 | GPT-5.5 | Gemini 3.1 Pro |
|---|---|---|---|
| Developer | Anthropic | OpenAI | Google DeepMind |
| Release date | May 28, 2026 | April 23, 2026 (API: April 24) | February 19, 2026 |
| API model ID | claude-opus-4-8 | gpt-5.5 | gemini-3.1-pro-preview |
| Context window | 1,000,000 tokens (200K on Microsoft Foundry) | ~1,050,000 tokens (400K inside Codex) | 1,048,576 tokens |
| Max output tokens | 128,000 | 128,000 | 65,536 |
| Input price per 1M tokens | $5.00 | $5.00 | $2.00 (up to 200K context) |
| Output price per 1M tokens | $25.00 | $30.00 | $12.00 (up to 200K context) |
| Speed/priority option | Fast mode: $10 / $50 per 1M, ~2.5x speed | Priority processing: 2.5x standard rate | Not publicly published |
| Input modalities | Text, image, code | Text, image | Text, image, audio, video, PDF (up to 1,000 pages) |
| Architecture notes | Dense transformer with selectable effort levels (high/extra/max) | Not publicly disclosed | Mixture-of-Experts with three-tier thinking (low/medium/high) |
| Primary platforms | Claude API, Amazon Bedrock, Vertex AI, Microsoft Foundry, AWS GovCloud, Claude.ai | ChatGPT, Codex, OpenAI API, Amazon Bedrock, Microsoft Foundry | Gemini app, Google AI Studio, Vertex AI, Gemini CLI, Antigravity, Android Studio, NotebookLM |
Two things stand out immediately. First, all three models converged on roughly the same 1 million token context window, which was a genuine differentiator as recently as 2025 and is now table stakes. Second, Gemini 3.1 Pro is priced meaningfully lower on both input and output tokens, a gap that compounds fast for anyone running high-volume batch jobs rather than single chat sessions.
Release Timeline: How Three Labs Shipped Flagships 14 Weeks Apart
Gemini 3.1 Pro arrived first, on February 19, 2026, as an incremental but sizable jump over Gemini 3 Pro. The headline gain was reasoning: Gemini 3.1 Pro scored 77.1% on the ARC-AGI-2 abstract reasoning benchmark, according to Meta Intelligence’s technical analysis, up from 31.30.8 for the prior Gemini 3 Pro, a 46.3-point jump the report calls about a 150.3% relative improvement.
GPT-5.5 followed on April 23, arriving first inside ChatGPT and Codex for Plus, Pro, Business, and Enterprise users, with API access landing the next day. OpenAI said the delay came down to additional safety and security review before opening the model to third-party developers, a pattern that has repeated across several of its 2026 releases.
Claude Opus 4.8 closed the cycle on May 28, six weeks after Opus 4.7. Anthropic’s release notes point to gains concentrated in agentic coding and long-running tasks rather than a ground-up rebuild: SWE-bench Pro climbed from 64.3% to 69.2%, and the GDPval-AA knowledge-work score rose from 1,753 to 1,890 Elo, per CloudZero’s benchmark breakdown. Anthropic kept pricing identical to Opus 4.7 at $5 input and $25 output per million tokens, which is unusual in a market where most vendors raise prices alongside capability.
The pattern across all three releases: shorter gaps between major versions, smaller headline jumps per release, and heavier emphasis on agentic and tool-use benchmarks over the general knowledge tests that dominated comparisons in 2023 and 2024.
Pricing Compared: API Rates and Consumer Subscriptions
On paper, entry-level consumer pricing is nearly identical across all three: ChatGPT Plus, Claude Pro, and Google AI Pro all land within a dollar of $20 a month. The real spread shows up in API pricing and at the top of each consumer tier.
| Tier | ChatGPT / OpenAI | Claude.ai / Anthropic | Gemini / Google |
|---|---|---|---|
| Free | $0 | $0 | $0 |
| Budget tier | Go: $8/month | Not offered | Not offered |
| Entry paid tier | Plus: $20/month | Pro: $20/month ($17/month billed annually) | Google AI Pro: $19.99/month |
| Power-user tier | Pro: $200/month | Max 5x: $100/month; Max 20x: $200/month | Google AI Ultra: $249.99/month |
| Team / Business | Business: $25/user/month ($20 annual) | Team Standard: $25/seat/month; Team Premium: $125/seat/month | Custom via Google Workspace |
| Enterprise | Custom | Custom | Custom |
| API input (per 1M tokens) | $5.00 | $5.00 | $2.00 |
| API output (per 1M tokens) | $30.00 | $25.00 | $12.00 |
Pricing sourced from OpenAI’s pricing page, Anthropic’s plan comparison, and Google’s AI plans page.
The widest gap sits in output token pricing: GPT-5.5 charges $30 per million output tokens against $12 for Gemini 3.1 Pro, an $18 spread that adds up fast for reasoning-heavy workloads that generate long responses. Claude sits in the middle at $25. All three also penalize long context differently. Gemini 3.1 Pro doubles its rate past 200,000 tokens in a single request, to $4 input and $18 output. GPT-5.5 applies a 2x input and 1.5x output multiplier once a prompt crosses 272,000 tokens. Claude Opus 4.8 holds its flat $5/$25 rate across the full 1 million token window, which makes it the more predictable choice for teams that regularly submit very large prompts.
At the top of the consumer stack, Google AI Ultra’s $249.99 a month is the most expensive single-user plan of the three, about $50 above ChatGPT Pro and Claude Max 20x, both of which sit at $200. Anthropic’s Team plans are the most fragmented, splitting into a $25 Standard seat with web-only access and a $125 Premium seat that adds Claude Code, a gap worth checking before provisioning seats for a whole engineering org.
Prompt Caching and Hidden Cost Levers
Sticker price per token only tells part of the story. All three providers offer prompt caching, which reuses the processed state of a repeated prompt prefix (a system prompt, a long document, a codebase snapshot) instead of re-processing it on every call, and the discount for a cache hit varies enough to change which model is actually cheapest for a given workload. Gemini 3.1 Pro’s cached-context rate drops to roughly $0.50 per million tokens on a cache hit against its $2.00 standard input rate, a 75% discount, according to pricing tables published by nxcode.io’s Gemini 3.1 Pro guide. That makes Gemini the strongest option for applications that send the same large system prompt or reference document thousands of times a day, such as a customer support bot grounded in a fixed knowledge base.
Claude Opus 4.8 lowered its minimum cacheable prompt length to 1,024 tokens, down from a higher threshold on Opus 4.7, according to Simon Willison’s write-up of the release, which means shorter prompts now qualify for caching discounts than before. GPT-5.5 takes a different approach entirely, discounting entire request classes rather than just repeated prefixes: Batch and Flex processing run at half the standard API rate for workloads that can tolerate delayed responses, while Priority processing costs 2.5 times the standard rate for applications that need guaranteed low latency, per OpenAI’s GPT-5.5 announcement.
The practical takeaway is that none of the three published sticker prices reflect what a real production workload actually pays. A support chatbot that reuses a 5,000-token system prompt on every call behaves completely differently, cost-wise, on Gemini’s caching discount than it does on Claude’s flat rate or GPT-5.5’s batch tier. Anyone comparing these three models on price alone should model their actual call pattern, not just the headline per-token numbers, before picking a winner.
Benchmark Performance: SWE-Bench, GPQA Diamond, and Agentic Tasks
Benchmark scores below are pulled directly from each vendor’s own release material or from third-party trackers that publish their methodology, cross-checked across multiple sources where possible. Cells marked “not published” mean the vendor has not released a directly comparable score, not that the model scored zero.
| Benchmark | What it tests | Claude Opus 4.8 | GPT-5.5 | Gemini 3.1 Pro |
|---|---|---|---|---|
| SWE-bench Verified | Resolving real GitHub issues | 88.6% | Not published | Not published |
| SWE-bench Pro | Harder, multi-file engineering tasks | 69.2% | Not published | Not published |
| GPQA Diamond | Graduate-level science reasoning | Not published | Not published | 94.3% |
| MMLU Pro | Broad academic knowledge | Not published | Not published | 91% |
| ARC-AGI-2 | Abstract, novel-pattern reasoning | Not published | Not published | 77.1% |
| Terminal-Bench 2.0 | Command-line, agentic tool use | Not published | 82.7% | Not published |
Sources: Anthropic’s Opus 4.8 announcement, Simon Willison’s independent write-up, Google Cloud’s Gemini 3.1 Pro model page, and third-party trackers HokAI and AI Automation Global for the Terminal-Bench 2.0 figure.
The pattern that jumps out is that each lab picked a different lane to lead in, and none of the three has published a head-to-head score on the other two’s signature benchmark. That is not an accident. Vendors tend to headline the evaluation where they win and stay quiet on the rest. Two figures worth adding outside the table: Claude Opus 4.8’s Artificial Analysis Intelligence Index score of 61.4, a blended measure across more than ten evaluations that currently ranks Anthropic’s model first overall, and its OSWorld computer-use score of 83.4%, which CloudZero reports as a 7-point lead over Gemini 3.1 Pro and a 4.7-point lead over GPT-5.5 on the same test. On raw math ability, Claude Opus 4.8 also posted 96.7% on the USAMO olympiad benchmark, a category OpenAI and Google have not published directly comparable Opus-era figures for.
Context Windows, Output Limits, and Multimodal Support
All three models now sit at roughly 1 million tokens of input context, which covers something like 750,000 words or a mid-size codebase in a single request. The differences show up at the edges. Claude Opus 4.8 and GPT-5.5 both cap output at 128,000 tokens, double Gemini 3.1 Pro’s 65,536-token ceiling. For workloads that generate long documents, extensive code diffs, or lengthy transcripts in a single response, that gap matters more than the input side.
Multimodal support is where Gemini 3.1 Pro separates itself. Google built native support for video up to roughly 45 minutes with audio (or about an hour without), up to 10 videos per prompt, and PDF documents up to 1,000 pages, according to Google Cloud’s technical documentation. Claude Opus 4.8 handles text, image, and code input, and GPT-5.5 adds image input to its text core, but neither ships the same native audio and video ingestion Google built into Gemini from the ground up. For teams building on transcripts, security camera footage, lecture recordings, or scanned legal archives, that capability gap alone can decide the choice regardless of price.
Context pricing also behaves differently once requests get large. Gemini 3.1 Pro’s rate doubles past 200,000 tokens in a single call. GPT-5.5 steps up 2x on input and 1.5x on output past 272,000 tokens. Claude Opus 4.8 is the only one of the three that holds a flat rate across its entire context window, which simplifies cost forecasting for applications that regularly submit near-maximum prompts, even though its baseline per-token price sits above Gemini’s.
Coding and Agentic Workflows Compared
Coding is where this comparison gets the most attention, and it is also where the three models diverge most sharply in how they were tuned. Claude Opus 4.8’s 88.6% on SWE-bench Verified and 69.2% on the harder SWE-bench Pro make it the strongest publicly benchmarked option for autonomous, multi-step engineering work, resolving roughly seven in ten complex real-codebase tasks without human intervention on the harder test. GitHub made Opus 4.8 generally available inside GitHub Copilot on its launch day, and Anthropic’s own Opus product page recommends the model specifically for production-ready code and complex agent workflows where quality matters more than raw speed. For a closer look at how Claude’s coding-specific tooling stacks up against OpenAI’s, see our Claude Code vs Codex comparison.
Opus 4.8 also shipped a new agentic feature called Dynamic Workflows, which lets the model split a large task across parallel sub-agents instead of working through it in a single linear thread. It ships on by default for Max, Team, and API users, but off by default for Enterprise accounts, where an administrator has to switch it on manually, a rollout choice that suggests Anthropic is being deliberately cautious about handing autonomous, multi-agent execution to large organizations before they opt in. It is available today in the Claude Code CLI, the desktop app, and the VS Code extension.
GPT-5.5 was tuned around a different kind of coding work: terminal sessions, shell commands, and agentic tool chains. Its 82.7% on Terminal-Bench 2.0 reflects that focus, and OpenAI’s strategy leans into tighter integration between ChatGPT, Codex, and its Atlas AI browser, aiming for what amounts to a single AI surface that understands a company’s codebase, documents, and desktop context at once. GPT-5.5 also shipped with built-in computer use, hosted shell access, and an “apply patch” tool purpose-built for agentic coding sessions, according to OpenAI’s API changelog.
Gemini 3.1 Pro takes a third path, embedding directly into the developer tools Google already controls: Android Studio, the Gemini CLI, and Google’s newer Antigravity IDE. That distribution matters more than raw benchmark scores for Android and Google Cloud-native teams, since the model shows up inside tools they are already running rather than requiring a new integration. Google has not published a SWE-bench Verified score for Gemini 3.1 Pro directly comparable to Anthropic’s and OpenAI’s figures, which makes head-to-head coding comparisons harder to verify independently.
Reasoning, Knowledge Work, and Where Each Model Struggles
On graduate-level science and abstract reasoning, Gemini 3.1 Pro currently posts the strongest published numbers of the three: 94.3% on GPQA Diamond and 77.1% on ARC-AGI-2, a benchmark specifically designed to resist memorization by testing novel pattern recognition rather than recalled facts. Google’s three-tier thinking system, which lets the model allocate more or less computation depending on task difficulty, is the architectural choice behind that jump, according to Meta Intelligence’s analysis of the release.
Claude Opus 4.8 leads on the knowledge-work side instead. Its GDPval-AA score, a benchmark built around real-world professional and economic tasks rather than academic tests, rose to 1,890 Elo, and legal AI platform Harvey reported 91.1% on BigLaw Bench after putting Opus 4.8 into production on its launch day, per O-mega’s benchmark guide. That combination, strong professional-task performance plus a 1,890 Elo knowledge-work score, is why Anthropic’s model shows up disproportionately often in legal, financial, and consulting deployments rather than pure research settings.
None of the three is free of trade-offs. GPT-5.5’s rate-limit system is the most complex of the three, with five numbered tiers ranging from 500 requests per minute and 500,000 tokens per minute at the bottom to 15,000 requests per minute and 40 million tokens per minute at the top, according to OpenAI’s own API documentation, meaning new developer accounts start well below what a production deployment usually needs and have to earn higher tiers over time. Gemini 3.1 Pro’s 65,536-token output ceiling can force teams to chunk long-form generation tasks that Claude and GPT-5.5 handle in a single call. And Claude Opus 4.8, while efficient in output tokens, remains the priciest of the three on input tokens tied with GPT-5.5, with no budget-tier equivalent to ChatGPT Go for cost-sensitive individual users.
Real-World Deployments: 7 Examples From Production
Benchmark scores are one thing. Production usage is another. Here is what is publicly confirmed about each model in live deployments as of June 2026.
- Government of Alberta has used Claude Code, running on Opus and Sonnet-class models, since 2025 to review government systems and find and fix security vulnerabilities, according to Anthropic.
- GitHub Copilot made Claude Opus 4.8 generally available to Copilot users on its May 28 launch day, citing stronger code understanding and generation.
- Harvey, a legal AI platform, put Claude Opus 4.8 into production the same day it launched and reported a 91.1% score on BigLaw Bench, a legal-reasoning benchmark.
- PwC is deploying Claude to build agentic tools for its engineering teams and to embed AI directly into dealmaking workflows.
- Notion, Asana, and Sentry are listed as early production users of Claude models inside their own products, per Anthropic’s developer newsletter coverage.
- Amazon Bedrock made GPT-5.5 and GPT-5.4 generally available for production workloads in June 2026, giving enterprise AWS customers the same governance and operational controls they already use for other Bedrock models.
- Google’s own product line is arguably the largest production deployment of Gemini 3.1 Pro: it ships inside Android Studio, the Gemini CLI, the Antigravity IDE, NotebookLM, and Vertex AI, putting it in front of millions of developers who may never open the standalone Gemini app.
The pattern here is informative on its own. Anthropic’s named case studies skew toward regulated, high-stakes work: government security, corporate legal, and professional services. OpenAI’s strongest public signal is infrastructure-level (Bedrock general availability) rather than named customer logos. Google’s biggest deployment story is internal distribution through its own developer tools rather than third-party enterprise wins.
Consumer Apps Compared: ChatGPT vs Claude.ai vs the Gemini App
For everyday, non-API use, the three apps diverge in free-tier generosity and in what each one bundles beyond chat. ChatGPT’s free tier allows roughly 10 messages every five hours before falling back to a smaller model, with a 16,000-token context window, according to OpenAI’s help center. Claude’s free tier is looser day to day, offering 30 to 100 messages daily with a four-to-eight-hour reset window depending on demand, but it locks out Claude Code, Anthropic’s terminal coding tool, entirely.
Paid tiers bundle different extras. ChatGPT Plus adds Sora video generation credits, Deep Research (limited to 10 uses a month on Plus), Codex access, and roughly 320-page effective context. Claude Pro adds Claude Code in the terminal along with file creation and code execution inside the browser. The Gemini app leans on Google’s ecosystem instead of standalone features: Google AI Pro and Ultra subscribers get Gemini 3.1 Pro access baked into Gmail, Docs, Sheets, and NotebookLM rather than a separate destination app being the main draw.
None of the three free tiers gives access to the flagship model without limits. Free ChatGPT and free Gemini both fall back to smaller models after a usage cap, and Claude’s free tier runs on Sonnet and Haiku-class models rather than Opus. Anyone evaluating “Claude vs Gemini vs ChatGPT” head to head on the free tier alone is not actually comparing Opus 4.8, GPT-5.5, or Gemini 3.1 Pro, since none of the three companies gives free users direct access to their top model.
Enterprise and Cloud Availability: Bedrock, Vertex AI, and Microsoft Foundry
Cloud distribution strategy is one of the sharpest differences between the three labs, and it says a lot about how each expects to win enterprise deals. Claude Opus 4.8 is the most widely distributed of the three, available through Anthropic’s own API, Amazon Bedrock, Google Cloud’s Vertex AI, Microsoft Foundry, and even AWS GovCloud (US) for government workloads that require FedRAMP-aligned infrastructure.
GPT-5.5 follows a similar multi-cloud pattern, landing on Amazon Bedrock and Microsoft Foundry alongside OpenAI’s own API, though it skips Google’s Vertex AI entirely, an unsurprising gap given the direct competition between OpenAI and Google. Gemini 3.1 Pro takes the most Google-centric approach of the three: it is available through Vertex AI and the Gemini Enterprise Agent Platform, but not through Bedrock or Foundry, keeping enterprise distribution inside Google’s own cloud rather than spreading across competitors’ infrastructure the way Anthropic and OpenAI have both chosen to do.
For enterprise buyers already committed to a single cloud vendor, that distribution pattern can matter more than benchmark scores. An AWS-first shop can run either Claude Opus 4.8 or GPT-5.5 through Bedrock with identical billing and governance tooling. A Google Cloud-first shop gets Gemini 3.1 Pro natively but has to route around Vertex AI to reach the other two models.
5 Use Cases and Which Model Fits Each One
Benchmark leadership does not automatically translate into the right choice for a given project. Here is how the trade-offs break down by workload.
- Long-running agentic coding and large-codebase refactors: Claude Opus 4.8. The 88.6% SWE-bench Verified score, 69.2% on the harder SWE-bench Pro, and 35% fewer output tokens than its predecessor add up to the strongest option for autonomous, multi-step engineering work.
- Terminal-heavy developer tooling already built on Codex or Atlas: GPT-5.5. Its Terminal-Bench 2.0 lead and native computer-use, hosted shell, and apply-patch tools were purpose-built for this workflow.
- Video, audio, and long-document analysis at volume: Gemini 3.1 Pro. Native support for hour-long video, 1,000-page PDFs, and the lowest per-token price of the three make it the clear pick for high-volume multimodal pipelines.
- Regulated or government workloads requiring FedRAMP-aligned or compliance-heavy infrastructure: Claude Opus 4.8 or GPT-5.5, both available through AWS GovCloud or Microsoft Foundry. Gemini 3.1 Pro stays inside Google Cloud’s own compliance framework instead.
- Budget-conscious, high-volume API applications: Gemini 3.1 Pro, at $2 input and $12 output per million tokens against $5 and $25 to $30 for the other two, a difference that compounds quickly at production scale.
- Legal, financial, and professional document review: Claude Opus 4.8, backed by its 1,890 Elo GDPval-AA score and Harvey’s reported 91.1% on BigLaw Bench in live production use.
Migration Guide: Switching Between Claude, GPT-5.5, and Gemini
Switching flagship models is rarely a one-line config change, even though the three providers’ SDKs look superficially similar. A sensible migration checklist, adapted from guidance Anthropic published alongside the Opus 4.8 launch, applies to any cross-provider move: sample 20 to 50 real tasks from production traffic, replay them on the target model at matched effort or reasoning-effort settings, compare end-to-end cost and output quality side by side, and only then re-tune prompts that assume a specific model’s quirks.
The SDK call shapes differ enough to matter for engineering time, even before quality differences enter the picture:
# Claude Opus 4.8 (Anthropic SDK)
import anthropic
client = anthropic.Anthropic()
response = client.messages.create(
model="claude-opus-4-8",
max_tokens=1024,
messages=[{"role": "user", "content": "Summarize the migration risks in 3 sentences."}]
)
# GPT-5.5 (OpenAI SDK, Responses API)
from openai import OpenAI
client = OpenAI()
response = client.responses.create(
model="gpt-5.5",
input="Summarize the migration risks in 3 sentences."
)
# Gemini 3.1 Pro (Google GenAI SDK)
from google import genai
client = genai.Client()
response = client.models.generate_content(
model="gemini-3.1-pro-preview",
contents="Summarize the migration risks in 3 sentences."
)Beyond syntax, three gotchas trip up most migrations. Long-context pricing thresholds differ (200K for Gemini, 272K for GPT-5.5, no threshold at all for Claude within its 1M window), so a workload that was cost-neutral on one provider can jump in price on another once prompts cross those lines. Output ceilings differ too: a pipeline built around GPT-5.5 or Claude’s 128,000-token output limit will need chunking logic added if it moves to Gemini 3.1 Pro’s 65,536-token cap. And rate-limit tiers are not portable. A team that earned OpenAI’s Tier 5 access (15,000 requests per minute) through months of usage history starts over on Anthropic’s or Google’s own tiering system when it switches providers.
For teams unsure whether to migrate at all, running a small pilot on one genuinely representative workflow, rather than a full cutover, is the approach both Anthropic’s and third-party migration guides converge on.
Pros and Cons of Each Model
Claude Opus 4.8: Pros and Cons
- Pro: Highest published SWE-bench Verified score (88.6%) among the three for autonomous coding work
- Pro: Flat token pricing across the full 1M context window, with no long-context price step-up
- Pro: Widest cloud distribution, including AWS GovCloud for regulated workloads
- Con: No budget consumer tier equivalent to ChatGPT Go
- Con: Free tier does not include Claude Code or Opus-class model access
- Con: Tied for the highest input token price of the three at $5 per million
GPT-5.5: Pros and Cons
- Pro: Leads Terminal-Bench 2.0, the strongest published score for command-line agentic tasks
- Pro: Deepest integration across ChatGPT, Codex, and the Atlas browser as one connected surface
- Pro: Only provider offering a sub-$10 consumer tier (Go, at $8 a month)
- Con: Highest output token price of the three at $30 per million
- Con: Most complex rate-limit system, with new accounts starting well below production-ready throughput
- Con: Not available on Google’s Vertex AI, limiting multi-cloud flexibility for Google-first shops
Gemini 3.1 Pro: Pros and Cons
- Pro: Cheapest input and output token pricing of the three, by a wide margin
- Pro: Only model with native long-video, audio, and 1,000-page PDF ingestion
- Pro: Leads GPQA Diamond (94.3%) and ARC-AGI-2 (77.1%) reasoning benchmarks
- Con: Lowest max output ceiling at 65,536 tokens, roughly half of the other two
- Con: Most expensive top consumer tier at $249.99 a month for Google AI Ultra
- Con: Not available through Amazon Bedrock or Microsoft Foundry, limiting it to Google Cloud infrastructure
The Verdict: Which Model Wins in June 2026
There is no single winner, and any comparison that claims otherwise is oversimplifying three models built with genuinely different priorities. Claude Opus 4.8 is the strongest choice for autonomous coding and professional knowledge work, backed by the highest SWE-bench Verified score of the three (88.6%), the top spot on the Artificial Analysis Intelligence Index (61.4), and the widest enterprise cloud distribution. GPT-5.5 is the strongest choice for teams already inside OpenAI’s developer ecosystem, leading Terminal-Bench 2.0 and offering the tightest Codex and Atlas integration, though it carries the highest output pricing and the most complex rate-limit ramp. Gemini 3.1 Pro is the strongest choice on cost and multimodal reach, running at roughly 40 to 60% of the token price of its rivals while remaining the only one of the three built natively for long video and large PDF workloads.
The most useful way to read this comparison is by workload rather than by aggregate score. A team should ask what they are automating, check which benchmark actually maps to that task, and price out their expected token volume against all three rate cards before committing. Given how fast this market is moving, with three major releases inside a 14-week window already this year, treating any single verdict as permanent would be a mistake. The next flagship update from one of these three labs is likely only weeks away.
Frequently Asked Questions
Which is cheaper, Claude, ChatGPT, or Gemini?
Gemini 3.1 Pro is the cheapest on API pricing at $2 per million input tokens and $12 per million output tokens, compared with $5 input for both Claude Opus 4.8 and GPT-5.5, and $25 to $30 output. On consumer subscriptions, entry-level pricing is nearly identical: ChatGPT Plus and Claude Pro are both $20 a month, and Google AI Pro is $19.99. OpenAI is the only one of the three offering a true budget tier, ChatGPT Go, at $8 a month.
Which model is best for coding, Claude Opus 4.8 or GPT-5.5?
Claude Opus 4.8 has the higher published SWE-bench Verified score (88.6% vs. no directly comparable published figure from OpenAI) and is Anthropic’s own recommended model for production-ready code. GPT-5.5 leads a different coding benchmark, Terminal-Bench 2.0, at 82.7%, making it the stronger option specifically for terminal and command-line-heavy agentic workflows rather than broad software engineering tasks.
Can I use Claude, ChatGPT, and Gemini for free?
All three offer free tiers, but none gives free users access to the flagship model discussed in this comparison. Free ChatGPT runs a smaller model after roughly 10 messages every five hours. Free Claude.ai runs on Sonnet and Haiku-class models rather than Opus. Free Gemini falls back to a lighter model after usage limits. Evaluating the free tiers is not equivalent to testing Claude Opus 4.8, GPT-5.5, or Gemini 3.1 Pro directly.
Which model has the largest context window?
All three sit close to 1 million tokens: Gemini 3.1 Pro at 1,048,576, GPT-5.5 at roughly 1,050,000 (reduced to 400,000 inside Codex), and Claude Opus 4.8 at 1,000,000 (reduced to 200,000 on Microsoft Foundry specifically). On maximum single-response output, Claude Opus 4.8 and GPT-5.5 both allow up to 128,000 tokens, double Gemini 3.1 Pro’s 65,536-token limit.
Which model is best for enterprise or regulated industries?
Claude Opus 4.8 has the broadest regulated-industry footprint of the three, including availability on AWS GovCloud (US) for government workloads, alongside Bedrock, Vertex AI, and Microsoft Foundry. GPT-5.5 covers Bedrock and Microsoft Foundry but not Vertex AI or GovCloud specifically. Gemini 3.1 Pro stays within Google Cloud’s own compliance and Vertex AI infrastructure rather than distributing across competing clouds.
How often do these models get updated?
All three labs are now shipping flagship updates roughly every six to ten weeks. Gemini 3.1 Pro (February 19), GPT-5.5 (April 23), and Claude Opus 4.8 (May 28) all launched within the same 14-week window in 2026, and Claude Opus 4.8 itself arrived just six weeks after Opus 4.7, illustrating how compressed the release cycle has become.
Does long context cost more on any of these models?
Yes, on two of the three. Gemini 3.1 Pro’s price doubles past 200,000 tokens in a single request, from $2/$12 to $4/$18 per million input/output tokens. GPT-5.5 applies a 2x input and 1.5x output multiplier once a prompt exceeds 272,000 tokens. Claude Opus 4.8 holds a flat $5/$25 rate across its entire 1 million token context window, with no long-context surcharge.
Which model handles video and audio input best?
Gemini 3.1 Pro is the clear choice for multimodal input beyond text and images. It natively processes video up to roughly 45 minutes with audio (or about an hour without), up to 10 videos per prompt, and PDF documents up to 1,000 pages. Neither Claude Opus 4.8 nor GPT-5.5 currently matches that native audio and video ingestion range.
Which model has the best prompt caching discount?
Gemini 3.1 Pro offers the deepest published caching discount, dropping to roughly $0.50 per million tokens on a cache hit against its $2.00 standard input rate, a 75% reduction. Claude Opus 4.8 lowered its minimum cacheable prompt length to 1,024 tokens, letting shorter prompts qualify for caching savings than under Opus 4.7. GPT-5.5 discounts by request type instead of by cached prefix, offering Batch and Flex processing at half the standard rate for delay-tolerant workloads.
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