xAI and Google shipped competing frontier models within two weeks of each other this spring, and the price tags could not look more different. Grok 4.3 landed on April 30, 2026 at $1.25 per million input tokens and $2.50 per million output tokens. Gemini 3.1 Pro arrived as a preview in May at $2.00 input and $12.00 output for prompts under 200K tokens, climbing to $4.00 and $18.00 beyond that. Run the same workload through both APIs and Gemini can cost roughly 2.7x to 4.8x more, depending on how much of your traffic is output-heavy.
That gap is why “grok vs gemini” has turned into one of the most searched AI comparisons of the summer. Engineering teams choosing a model for a new agentic pipeline need more than a marketing page, they need the actual SWE-bench numbers, the actual context window limits, and an honest read on where each vendor’s published benchmarks diverge from independent test results. This comparison pulls that data together: pricing, coding and reasoning scores from multiple evaluators, multimodal capabilities, agentic tooling, and a migration path if you’re already locked into one API and considering a switch.
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Grok 4.3 vs Gemini 3.1 Pro: The Quick Verdict
Neither model wins outright. Grok 4.3 is the cheaper, faster option built for high-volume agentic workloads, native video understanding, and real-time data access through X. Gemini 3.1 Pro posts the higher reasoning scores, the only native audio pipeline of the two, and Google’s enterprise compliance track record, at a meaningfully higher per-token cost. If your workload is output-token-heavy (long code generations, detailed reports, multi-turn agent chains), the price gap alone may decide it. If your workload leans on graduate-level reasoning or audio input, Gemini 3.1 Pro’s benchmark lead is hard to ignore.
For a broader view of where both models sit against the rest of the field, our 2026 AI model rankings hub tracks the full leaderboard, and our recent breakdown of Claude Opus 4.8 vs GPT-5.5 vs Gemini 3.1 Pro covers how Gemini stacks up against the other two frontier leaders.
The decision mostly comes down to a question most procurement checklists skip: what does your traffic actually look like? A team running short customer-support replies has a very different cost profile than one generating long technical reports or reviewing entire pull requests. The tables and worked examples below are built so you can plug in your own token counts rather than rely on a generic per-model price comparison that ignores how input and output ratios shift the real bill.
Release Timeline and Development Background
xAI took an unusually quiet approach to Grok 4.3. The model entered beta on April 17, 2026 with no press release, and the general API rollout completed on April 30, 2026. It succeeds Grok 4.20, keeping a similar parameter scale while refining the underlying architecture and adding a December 2025 knowledge cutoff, which puts its factual recall closer to current events than earlier Grok generations managed. The two headline additions over Grok 4.20 are native video understanding, a first for xAI’s lineup, and structured document output.
Google shipped Gemini 3.1 Pro as a preview roughly two weeks earlier, positioning it as an incremental but meaningful step over Gemini 3 Pro. Google’s own materials describe stronger reasoning, coding, and agentic performance for complex multimodal work, and the release focused on production enterprise workloads rather than a consumer-facing splash. Unlike xAI’s silent rollout, Google published benchmark tables alongside the release, though as we’ll get into below, independent testers have not always been able to reproduce Google’s own coding numbers.
Both companies are shipping on a roughly six-to-eight-week cadence at this point. That pace is part of why a straight Grok 4.3 vs Gemini 3.1 Pro comparison has a shorter shelf life than comparisons did in 2024: by the time most teams finish evaluating one pair of models, a new point release is already in beta.
The naming itself hints at how each company thinks about releases. xAI treats 4.3 as a minor version bump on top of Grok 4.20, the kind of update that used to ship as a silent model-card revision rather than a headline launch. Google’s “Preview” label on Gemini 3.1 Pro signals the opposite intent: a release meant to collect feedback from enterprise customers before a final, non-preview version locks in pricing and behavior. That distinction matters for procurement. Teams building on a preview model should expect some benchmark numbers and pricing terms to shift before general availability, while Grok 4.3’s GA status means xAI has already committed to the specs in the table above.
Vercel’s AI Gateway, which routes requests across multiple model providers, added Grok 4.3 to its supported models list within days of the xAI rollout, a fast turnaround that suggests infrastructure vendors saw enough demand to prioritize integration ahead of a formal announcement from xAI itself.
Full Specs Comparison: Grok 4.3 vs Gemini 3.1 Pro
Here is every major spec side by side, pulled from vendor documentation, OpenRouter’s benchmark pages, and independent comparison databases including LLM-Stats.
| Spec | Grok 4.3 (xAI) | Gemini 3.1 Pro (Google) |
|---|---|---|
| Release date | April 30, 2026 (GA) | May 2026 (Preview) |
| Knowledge cutoff | December 2025 | Not publicly specified |
| Input price (per 1M tokens) | $1.25 | $2.00 (up to 200K context) |
| Output price (per 1M tokens) | $2.50 | $12.00 (up to 200K context) |
| Extended-context pricing | Flat rate, no tier change | $4.00 in / $18.00 out beyond 200K |
| Context window (input) | 1,000,000 tokens | 1,000,000 tokens |
| Max output tokens | Not publicly capped | 64,000 tokens |
| SWE-bench Verified (vendor-published) | ~73% | 80.6% |
| SWE-bench Verified (independent runs) | ~75% | 69.6%-75.6% |
| GPQA Diamond | Not published for this point release | 94.3% |
| Artificial Analysis Intelligence Index | 53 | 57 |
| MMLU | Not published for this point release | 92.6% |
| Output speed | ~159-207 tokens/sec | ~120-130 tokens/sec |
| Native multimodal input | Text, image, video | Text, image, video, audio |
| Native image/audio generation | No | No (understanding only, not generation) |
| Agentic tooling | 16-agent “Heavy” mode, function calling, prompt caching | Native tool use, agentic coding workflows |
Two things jump out. First, both models converge on a 1-million-token input context window, so context length is no longer the differentiator it was a year ago. Second, Gemini 3.1 Pro caps output at 64,000 tokens per response while Grok 4.3 doesn’t publish an equivalent hard limit, which matters if your use case involves generating very long documents in a single call.
A third detail that’s easy to miss in a quick skim: the rows where data is simply unavailable tell you almost as much as the rows with hard numbers. Grok 4.3 hasn’t published GPQA Diamond or MMLU scores for this specific point release, and Gemini 3.1 Pro’s own team hasn’t broken agentic tool-use performance into a single headline metric the way xAI has with its Heavy-mode benchmarks. Treat any comparison chart that fills in those gaps with estimates, rather than leaving them blank, with some skepticism, since neither company has confirmed those numbers publicly as of this writing.
Pricing Breakdown: API Costs and Consumer Plans
Grok 4.3’s pricing is flat and simple: $1.25 per million input tokens, $2.50 per million output tokens, regardless of how much context you’re using. Gemini 3.1 Pro uses a tiered model that gets more expensive as your prompts grow past 200,000 tokens, jumping from $2.00/$12.00 to $4.00/$18.00 per million tokens. For teams running short, high-frequency requests, that tiering rarely kicks in. For teams stuffing entire codebases or long documents into a single call, it adds up fast.
| Pricing tier | Grok 4.3 | Gemini 3.1 Pro |
|---|---|---|
| Input, ≤200K context | $1.25 / 1M tokens | $2.00 / 1M tokens |
| Output, ≤200K context | $2.50 / 1M tokens | $12.00 / 1M tokens |
| Input, >200K context | $1.25 / 1M tokens (no change) | $4.00 / 1M tokens |
| Output, >200K context | $2.50 / 1M tokens (no change) | $18.00 / 1M tokens |
| Entry consumer tier | Standard SuperGrok, ~$30/month | Google AI Pro, ~$19.99/month (top reasoning modes may require a higher tier) |
| Top consumer tier | SuperGrok Heavy, $300/month (early exclusive access to 4.3) | Google AI Ultra (pricing varies by market) |
Worth a concrete example instead of just rate cards. Take a support-ticket triage pipeline processing 1,000 tickets a day, averaging 2,000 input tokens and 500 output tokens per ticket. On Grok 4.3, that’s $2.50 for input plus $1.25 for output, a total of $3.75 a day. Run the identical workload through Gemini 3.1 Pro’s sub-200K tier and it’s $4.00 for input plus $6.00 for output, or $10.00 a day. That’s Gemini costing about 2.7x more for this exact scenario, a smaller multiple than the raw output-price ratio because input tokens dilute the gap. Scale that to a million tickets a month and the difference between $112,500 and $300,000 stops being a rounding error on anyone’s infrastructure budget.
Now flip the workload to something output-heavy, like a code-generation agent producing 1,000 input tokens of context and 4,000 output tokens per request across 5,000 requests a day. Grok 4.3 charges $6.25 for input and $50.00 for output, a daily total of $56.25. Gemini 3.1 Pro charges $10.00 for input and a steep $240.00 for output, a daily total of $250.00, putting Gemini at roughly 4.4x the cost for a workload this skewed toward generation. That’s the pattern to watch: the more output-heavy your use case, the closer the real-world price gap tracks toward Gemini’s full 4.8x output-price multiple rather than the smaller blended ratio a balanced input/output workload produces.
Benchmark Deep Dive: SWE-Bench Verified
SWE-bench Verified is the benchmark most engineering teams actually care about because it tests real GitHub issues rather than synthetic coding puzzles. Google publishes an 80.6% score for Gemini 3.1 Pro on SWE-bench Verified.1 Pro, which would put it comfortably ahead of Grok 4.3’s roughly 73%. But that gap shrinks considerably once you bring in independent testing. Comparison database Tech Jacks Solutions’ side-by-side breakdown puts Gemini 3.1 Pro’s independently verified SWE-bench score anywhere from 69.6% to 75.54.2% depending on the test harness, while Grok 4.3’s independent runs land close to 75%.
That’s a meaningfully different story than the vendor headline numbers suggest. Read the raw press releases and Gemini looks like it’s winning coding by eight points. Read the independent evals and the two models are within a few points of each other, sometimes with Grok 4.3 slightly ahead. Google’s own supplementary metric, SWE-Bench Pro (a harder, less-contaminated variant of the benchmark), scores Gemini 3.1 Pro at 54.2%, which OpenRouter’s Grok 4.3 benchmark page doesn’t have a direct published equivalent for, making a clean apples-to-apples read difficult on that specific variant.
The practical takeaway for engineering leads: don’t pick a coding model off a single vendor slide. Run your own held-out test set of real tickets from your own repository. The published gap between Grok 4.3 and Gemini 3.1 Pro on coding tasks is smaller and noisier than either company’s marketing implies.
Benchmark Deep Dive: GPQA Diamond and Reasoning
Reasoning is where the two models genuinely diverge. Gemini 3.1 Pro scores 94.3% on GPQA Diamond, the graduate-level science reasoning benchmark, a number that’s been consistently reported across Google’s own materials, independent database LLM-Stats’ comparison tool, and third-party aggregators. Grok 4.3 has not published a comparable GPQA Diamond score for this specific point release. Grok 4 (the base model, not the 4.3 update) scored around 89% in earlier testing, but xAI hasn’t confirmed whether that figure carries over to 4.3.
On the Artificial Analysis Intelligence Index, which blends multiple reasoning and knowledge benchmarks into one composite score, Gemini 3.1 Pro sits at 57 against Grok 4.3’s 53, a four-point gap that tracks with the GPQA story. Grok 4.3 does lead on a couple of narrower benchmarks worth knowing about: it scores 98% on τ²-Bench Telecom, a domain-specific agentic reasoning test, and 81% on IFBench, which measures instruction-following precision. Neither of those is a general substitute for GPQA-style reasoning, but they matter if your use case looks like either of those domains.
If your workload involves multi-step scientific or legal reasoning where a wrong answer is costly, Gemini 3.1 Pro’s published lead here is real and reasonably well corroborated across sources. If your workload is closer to structured instruction-following or telecom-style agentic tasks, Grok 4.3’s narrower benchmark wins are worth weighing against the price difference.
Speed, Context Window, and Agentic Tool Use
Grok 4.3 is the faster model by a wide margin, generating roughly 159 to 207 tokens per second depending on the load and reasoning effort setting. Gemini 3.1 Pro runs closer to 120 to 130 tokens per second. For latency-sensitive applications like live chat or voice-adjacent interfaces, that gap is noticeable to end users, not just on a benchmark chart.
Both models now support a 1-million-token input context window, which has become table stakes at the frontier tier. The difference shows up on the output side: Gemini 3.1 Pro caps generation at 64,000 tokens per response, while Grok 4.3 doesn’t advertise an equivalent limit, giving it more headroom for tasks like generating a full test suite or a long technical document in one pass.
On agentic tooling, xAI built Grok 4.3 around a 16-agent “Heavy” mode that coordinates multiple reasoning threads on a single task, alongside standard function calling, structured outputs, and prompt caching to cut costs on repeated context. Gemini 3.1 Pro’s agentic story leans on Google’s broader Vertex AI tooling ecosystem and native tool use, with Google emphasizing “verified agentic coding scores” in its release materials, though the company hasn’t broken those out into a single comparable metric the way xAI’s Heavy mode benchmarks are reported.
Developer Experience: Docs, SDKs, and Rate Limits
Benchmarks get the headlines, but the day-to-day experience of building against an API depends just as much on documentation quality, SDK maturity, and how predictable rate limits are under load. Grok 4.3 ships through xAI’s own API alongside third-party gateways like OpenRouter, Vercel’s AI Gateway, AWS Bedrock, and Requesty, giving teams multiple routes to the same model without being locked into a single vendor’s client library. That multi-gateway availability arrived quickly, within the first couple of weeks of launch, which suggests xAI opened up distribution partnerships well before the public rollout.
Gemini 3.1 Pro’s primary path is Google’s own Generative Language API and Vertex AI, both backed by the documentation and IAM-based access control teams already using Google Cloud tend to expect. That’s a real advantage for organizations with existing GCP infrastructure, since model access, billing, and logging all flow through the same console rather than requiring a separate vendor relationship. Teams outside the Google Cloud ecosystem, by contrast, take on a bit more setup overhead to get equivalent observability compared to what a Grok 4.3 integration through an existing multi-model gateway can offer out of the box.
Neither company has published detailed public rate-limit tables for these specific releases, which is a common gap across the industry at the frontier tier. Teams evaluating either model for production traffic should request written rate-limit and burst-capacity commitments during procurement rather than relying on default tier limits, especially if the workload involves the kind of high-volume agentic use case where Grok 4.3’s pricing model is most attractive.
Multimodal Capabilities: Native Video vs Native Audio
This is the cleanest differentiator between the two models. Grok 4.3 introduced native video input, the first time xAI has shipped that capability, letting the model process video content directly rather than requiring you to extract frames or transcripts first. It still handles text and images natively but has no native audio or image generation.
Gemini 3.1 Pro covers more ground: text, image, video, and audio, all as native inputs. That audio piece is the meaningful gap. For workflows that need to process spoken content directly, whether that’s a customer call, a podcast, or a voice memo, Gemini is the only model in this comparison that handles it as a first-class signal without bolting on a separate transcription step first. Neither model generates images or audio natively, both are understanding-only on the multimodal side.
If your pipeline already includes a separate speech-to-text step, Grok 4.3’s missing native audio support may not matter much in practice. If you’re trying to cut a preprocessing stage out of your architecture entirely, Gemini 3.1 Pro’s native audio input removes a moving part.
5 Real-World Scenarios Where the Choice Matters
Benchmarks only tell part of the story, and a spec sheet doesn’t tell you how a model behaves inside an actual product. Here’s how the two models’ verified capabilities play out in scenarios engineering teams are actually building for in mid-2026, based on the pricing, speed, and multimodal differences documented above rather than hypothetical capabilities neither vendor has confirmed.
- Video bug-report triage. A support team receives screen-recording bug reports from customers. Grok 4.3’s native video input lets it watch the recording and summarize the reproduction steps directly, no frame extraction or manual review needed. Gemini 3.1 Pro can handle video too, but Grok’s flat, cheaper pricing makes it the more economical choice for high volumes of short recordings.
- Podcast and call-center audio indexing. A media company or call center needs to search and summarize thousands of hours of audio. Gemini 3.1 Pro’s native audio understanding processes the recordings directly, skipping a separate transcription API and the latency and cost that adds.
- High-volume agentic customer service. A SaaS company runs an agent that handles thousands of support conversations a day, each involving multiple tool calls and long output generations. Grok 4.3’s $2.50-per-million output price and 16-agent Heavy mode make the unit economics work at scale in a way Gemini’s $12 output price makes harder to justify.
- Legal and financial document review. A fintech or legal-tech team needs graduate-level reasoning across dense regulatory text. Gemini 3.1 Pro’s 94.3% GPQA Diamond score and higher Artificial Analysis Intelligence Index make it the safer default here, even at the higher per-token cost, because reasoning errors in this domain are expensive.
- CI-embedded code review. An engineering org wants a model to flag issues on every pull request. Given that independent SWE-bench testing shows Grok 4.3 and Gemini 3.1 Pro landing close together once you discount vendor-published numbers, this is a case where teams should run their own eval set rather than assume either model’s marketing claim, and where Grok’s lower cost per request tips the decision if the two are running close on your own repo.
- Real-time market or news monitoring. A trading desk or newsroom needs a model that can reason over fast-moving information and cross-reference it against live social data. Grok 4.3’s tie to X’s data firehose gives it a structural advantage here that Gemini 3.1 Pro, built on Google’s more traditional web and document indexing, doesn’t directly replicate.
Which Model Should You Choose? Use-Case Recommendations
Pulling the benchmark and pricing data together into direct recommendations:
- Choose Grok 4.3 if your workload is output-token-heavy and cost-sensitive, since the roughly 4.8x output price gap compounds quickly at scale.
- Choose Grok 4.3 if you need native video understanding without an external preprocessing pipeline.
- Choose Grok 4.3 if raw generation speed matters for your user experience, given its roughly 160-207 tokens/sec versus Gemini’s 120-130.
- Choose Gemini 3.1 Pro if your use case depends on verified graduate-level reasoning, where its 94.3% GPQA Diamond score and 57 Intelligence Index give it a real, corroborated edge.
- Choose Gemini 3.1 Pro if you need native audio understanding as a first-class input rather than a bolted-on transcription step.
- Choose Gemini 3.1 Pro if your organization already runs on Google Cloud or Vertex AI and values a single vendor relationship for billing, compliance, and support.
- Choose either, but test both, if your primary workload is code review or generation, since independent SWE-bench results put the two models closer together than either vendor’s own published numbers suggest.
Migration Guide: Switching Between Grok 4.3 and Gemini 3.1 Pro
Teams evaluating a switch, in either direction, tend to underestimate two things: the cost model recalculation and the prompt re-tuning. Here’s a practical path.
Step 1: Audit your current token usage
Pull 30 days of production logs and split your token usage into input and output. Because Grok 4.3 and Gemini 3.1 Pro price input and output so differently, a workload that looks cheap on one API can look expensive on the other. An output-heavy workload (long-form generation, verbose agent chains) sees the biggest swing when moving to Gemini’s $12-per-million output rate.
Step 2: Re-run your eval set, don’t trust the leaderboards
Given the gap between vendor-published and independent SWE-bench scores documented above, build a held-out set of 50-100 real examples from your own use case and score both models against it before committing.
Step 3: Update your request schema
Both providers expose chat-completion-style REST endpoints, but the request shape, authentication headers, and tool-calling schema differ. A simplified illustration of the structural differences:
// Grok 4.3 (xAI) — illustrative request shape
POST https://api.x.ai/v1/chat/completions
Authorization: Bearer $XAI_API_KEY
{
"model": "grok-4.3",
"messages": [{"role": "user", "content": "..."}],
"tools": [...],
"max_tokens": 8000
}
// Gemini 3.1 Pro (Google) — illustrative request shape
POST https://generativelanguage.googleapis.com/v1/models/gemini-3.1-pro:generateContent
x-goog-api-key: $GOOGLE_API_KEY
{
"contents": [{"role": "user", "parts": [{"text": "..."}]}],
"tools": [...],
"generationConfig": {"maxOutputTokens": 8000}
}Treat the snippet above as a structural guide, not production code. Field names and auth patterns change between API versions, so confirm against each vendor’s current documentation before shipping.
Step 4: Recalculate your cost model and canary the rollout
Apply your real input/output token ratio to both pricing tables above rather than assuming the headline per-token price tells the whole story. Once the numbers check out, roll the new model out to a small percentage of traffic first, watch for quality regressions and latency shifts, and keep a fast rollback path to the previous model while you validate.
Pros and Cons of Grok 4.3
Grok 4.3’s entire value proposition rests on the combination of low price and high throughput, with native video support layered on top as a genuine capability advantage rather than a marketing checkbox. The trade-offs mostly show up in areas xAI hasn’t fully documented yet, rather than in areas where the model has been tested and come up short.
Pros:
- Cheapest frontier-tier pricing on both input and output tokens
- Fastest raw generation speed of the two, at roughly 159-207 tokens/sec
- Native video input, a first for xAI’s model lineup
- 16-agent Heavy mode for complex multi-step agentic tasks
- Flat pricing regardless of context length, simplifying cost forecasting
Cons:
- Lower Artificial Analysis Intelligence Index (53 vs Gemini’s 57)
- No published GPQA Diamond score for this specific point release
- No native audio understanding
- Early access to the model was gated behind the $300/month SuperGrok Heavy tier
- xAI’s quiet, press-release-free rollout style makes it harder to track official capability claims
Pros and Cons of Gemini 3.1 Pro
Gemini 3.1 Pro’s case rests on corroborated reasoning strength and multimodal breadth rather than price. It’s the model for teams willing to pay a premium for the highest independently confirmed GPQA score in this comparison and a broader set of native inputs, backed by a vendor with a longer public safety-documentation track record.
Pros:
- Highest corroborated GPQA Diamond score of the pair, at 94.3%
- Full native multimodal input, including audio, which Grok 4.3 lacks
- Higher Artificial Analysis Intelligence Index (57)
- Backed by Google’s enterprise compliance and support infrastructure
- Cheaper entry-level consumer subscription tier
Cons:
- Output tokens cost roughly 4.8x more than Grok 4.3
- Pricing jumps further once prompts exceed 200,000 tokens
- Independent SWE-bench Verified scores run well below Google’s own published 80.6% figure
- Slower generation speed, around 120-130 tokens/sec
- Output capped at 64,000 tokens per response
Safety, Bias, and Enterprise Trust Considerations
Pricing and benchmarks aren’t the only inputs into a procurement decision. xAI’s Grok line has drawn public scrutiny over content moderation incidents in prior versions, a pattern some enterprise buyers weigh alongside raw benchmark performance when deciding whether to deploy a model in front of customers. xAI hasn’t published a detailed safety card for Grok 4.3 specifically, which makes it harder for security and compliance teams to evaluate against internal risk frameworks.
Google has a longer public track record of publishing model cards and safety evaluations alongside releases, and Gemini 3.1 Pro’s positioning toward “production enterprise workloads” reflects that emphasis. For regulated industries like finance, healthcare, or legal services, that documentation trail often matters as much as the benchmark scores themselves during vendor review.
Procurement teams should also weigh how each company handles data retention and training opt-outs, since policies differ by tier and can change between a preview release and general availability. Gemini 3.1 Pro’s preview status specifically means some enterprise terms, including data handling commitments, may still be provisional until Google finalizes the model. Grok 4.3, already at general availability, has locked-in terms, but xAI’s documentation on data handling has historically been thinner than Google’s, so this is worth confirming directly with either vendor’s sales or support team rather than assuming standard industry terms apply by default.
Neither point is a reason to rule a model out on its own, but both belong in the same spreadsheet as the pricing and SWE-bench numbers before a procurement decision gets made.
Where These Models Fit in the Wider 2026 Field
Grok 4.3 and Gemini 3.1 Pro aren’t competing in a vacuum. On the Artificial Analysis Intelligence Index, both sit behind the current frontier leaders: Claude Opus 4.8 leads the pack at 61.4, with GPT-5.5 close behind at 60.2. Gemini 3.1 Pro’s score of 57 puts it in the next tier down, ahead of Grok 4.3’s 53. That positioning adds context to the whole comparison. Grok 4.3 isn’t trying to out-reason Opus 4.8 or GPT-5.5, it’s competing primarily on price and speed, which is a different value proposition entirely. Our earlier coverage of Claude Opus 4.8 vs GPT-5.5 vs Gemini 3.1 Pro goes deeper into how those top two models compare on the same SWE-bench and GPQA benchmarks referenced throughout this piece.
On the open-weight side, models like GLM-5.2 and DeepSeek V4 Pro have closed much of the gap with closed frontier models on coding benchmarks specifically, which we covered in our GLM-5.2 vs DeepSeek V4 vs Kimi K2.6 comparison. That matters for the Grok vs Gemini decision indirectly: if your primary use case is coding and budget is the deciding factor, an open-weight model self-hosted on your own infrastructure is now a third option worth benchmarking alongside both Grok 4.3 and Gemini 3.1 Pro, not just a theoretical one.
The official SWE-bench project page is worth bookmarking directly if coding performance is your primary decision criterion. Vendor-published scores on this benchmark have diverged from independently reproduced results often enough across the industry in 2026 that treating the official leaderboard and methodology as a baseline, rather than any single company’s press release, is the more reliable way to evaluate new point releases as they ship.
The Verdict: Which AI Model Wins in June 2026?
There’s no single winner here, and treating this as a horse race misses the point. The data supports a split decision based on what you’re optimizing for.
| Category | Winner | Margin |
|---|---|---|
| Input token pricing | Grok 4.3 | 1.6x cheaper |
| Output token pricing | Grok 4.3 | 4.8x cheaper |
| GPQA Diamond / reasoning | Gemini 3.1 Pro | 94.3% vs unpublished |
| SWE-bench (vendor-published) | Gemini 3.1 Pro | ~8 points |
| SWE-bench (independent) | Roughly tied | Within a few points |
| Generation speed | Grok 4.3 | ~30-60% faster |
| Multimodal breadth | Gemini 3.1 Pro | Adds native audio |
| Consumer entry price | Gemini 3.1 Pro | ~$10/month cheaper |
For cost-sensitive, high-volume, output-heavy agentic workloads, Grok 4.3 is the more defensible default given the pricing gap alone. For reasoning-heavy or audio-native use cases, especially in regulated industries where Google’s compliance documentation carries weight, Gemini 3.1 Pro’s benchmark lead and multimodal breadth justify the premium. Teams building coding tools should treat the SWE-bench gap as smaller and noisier than either vendor’s headline number suggests, and should test both models against their own repository before choosing either one.
One more factor belongs in this calculation: both models will likely see another point release before most procurement cycles finish. Lock in your decision based on your own eval numbers and your own token economics, not on a single benchmark snapshot, since the gap documented here is a June 2026 picture of two models that are both still shipping updates every six to eight weeks.
Frequently Asked Questions
Is Grok 4.3 cheaper than Gemini 3.1 Pro?
Yes. Grok 4.3 charges $1.25 per million input tokens and $2.50 per million output tokens, versus Gemini 3.1 Pro’s $2.00 input and $12.00 output for prompts under 200,000 tokens. That’s roughly 1.6x cheaper on input and 4.8x cheaper on output.
Which model scores higher on GPQA Diamond?
Gemini 3.1 Pro, at 94.3%. Grok 4.3 has not published a GPQA Diamond score for this specific point release, so a direct comparison isn’t currently possible on that benchmark.
Does Grok 4.3 support audio input?
No. Grok 4.3 natively supports text, image, and video input, but not audio. Gemini 3.1 Pro is the only model of the two with native audio understanding.
What is the context window for each model?
Both models support a 1-million-token input context window. Gemini 3.1 Pro caps output generation at 64,000 tokens per response, while Grok 4.3 does not publish an equivalent output cap.
Which model is faster?
Grok 4.3, generating roughly 159 to 207 tokens per second compared to Gemini 3.1 Pro’s roughly 120 to 130 tokens per second.
Is Gemini 3.1 Pro actually better for coding than Grok 4.3?
It depends on whose numbers you trust. Google publishes an 80.6% SWE-bench Verified score for Gemini 3.1 Pro against roughly 73% for Grok 4.3. Independent test runs narrow that gap considerably, putting Gemini’s real-world score anywhere from 69.6% to 75.6%, close enough to Grok 4.3 that teams should test both against their own codebase rather than trust either vendor’s published number alone.
Which model is better for startups on a tight budget?
Grok 4.3, in most cases. Its flat, lower per-token pricing on both input and output makes cost forecasting simpler, and the gap widens further for any output-heavy workload like long-form generation or multi-turn agent chains.
Can I switch between Grok 4.3 and Gemini 3.1 Pro without rewriting my whole application?
Mostly, if your application is already built around an abstraction layer for model calls. The request schema, authentication method, and tool-calling format differ between the two APIs, so expect to update your integration layer and re-run your evaluation set, even if your core application logic stays the same.
Does Gemini 3.1 Pro cost more at every context length?
Yes, and the gap widens rather than narrows as context grows. Below 200,000 tokens, Gemini charges $2.00 input and $12.00 output per million tokens against Grok 4.3’s flat $1.25 and $2.50. Past 200,000 tokens, Gemini’s rate climbs to $4.00 and $18.00, while Grok 4.3’s pricing stays flat regardless of context length.
Which model has better multimodal support?
Gemini 3.1 Pro, on breadth. It natively handles text, image, video, and audio input. Grok 4.3 covers text, image, and video, but not audio, making Gemini the better fit for any pipeline that needs to process spoken content directly without a separate transcription step.
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