xAI and Anthropic released their latest flagship models eight days apart this spring, and the two companies made almost opposite bets on what a frontier model should be. Grok 4.3 reached xAI’s full API on April 30, 2026, priced at $1.25 per million input tokens and $2.50 per million output tokens. Claude Opus 4.8 followed on May 28, 2026, priced at $5 and $25 for the same two token types. Do the math and Claude’s output tokens cost exactly 10 times more than Grok’s.
That gap is a big part of the story, but not all of it. Grok vs Claude comes down to a real tradeoff between two different products: xAI is optimizing for cheap, fast, socially grounded reasoning, while Anthropic keeps charging a premium for what independent benchmarks show is a meaningfully stronger coding and agentic model. This comparison covers the full spec sheet, pricing at four realistic usage volumes, benchmark scores from Artificial Analysis and independent trackers, real-world use cases, a migration guide for teams switching either direction, and a verdict built on the numbers rather than vendor talking points. All figures below reflect data available as of June 23, 2026.
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What Is Grok 4.3?
Grok 4.3 is xAI’s flagship large language model. It rolled out in beta to SuperGrok Heavy subscribers on April 17, 2026, then reached the full API on April 30, 2026. It carries a 1 million token context window and supports text, image, and, according to xAI’s own model documentation, native video input, a feature that neither Claude Opus 4.8 nor most closed competitors match yet.
xAI priced Grok 4.3 aggressively from day one: $1.25 per million input tokens and $2.50 per million output tokens, according to pricing trackers including OpenRouter and PricePerToken. On Artificial Analysis’s Intelligence Index, a composite score built from a broad batch of reasoning and knowledge benchmarks, Grok 4.3 landed at 53 in the mid-June 2026 snapshot used throughout this article. It separately posted a GPQA Diamond score of 90.1% across several independent model-tracking pages, including CoderSera’s launch guide. xAI has not published a SWE-bench Verified score for Grok 4.3, so anyone weighing it purely for coding work is working with an incomplete picture on that specific benchmark.
Grok 4.3 also ships agentic tool use, long-document analysis, and the ability to generate files directly in formats like PDF, PPTX, and XLSX. For consumers rather than developers, xAI sells chat access through SuperGrok ($30 a month) and SuperGrok Heavy ($300 a month), though those subscription tiers are separate from the metered API pricing that matters for the rest of this comparison.
xAI is Elon Musk’s AI company, founded in 2023 and merged operationally with the X platform, which is exactly why Grok’s real-time data access isn’t a bolted-on feature but a structural part of the product. That ownership link matters for this comparison since it’s the reason Grok can plug into a live social feed in a way that a model trained purely on a static corpus can’t easily replicate.
What Is Claude Opus 4.8?
Claude Opus 4.8 is Anthropic’s flagship model, released May 28, 2026 at pricing unchanged from its predecessor, Opus 4.7: $5 per million input tokens and $25 per million output tokens, according to Anthropic’s own announcement and its official pricing documentation. Like Grok 4.3, it carries a 1 million token context window, but Anthropic also publishes a hard ceiling on generation length: 128,000 max output tokens per request, a number xAI hasn’t clearly documented for Grok 4.3.
Where Claude Opus 4.8 pulls ahead is coding and agentic reliability. Independent benchmark trackers put it at 88.6% on SWE-bench Verified and 69.69.2% on SWE-bench Pro, and it does not have a verified 1,890 Elo GDPval-AA leader figure in the provided sources. On the same mid-June 2026 Artificial Analysis Intelligence Index snapshot that scored Grok 4.3 at 53, Claude Opus 4.8 came in at 61.4, the highest of any model tracked that week.
Anthropic has built its brand around a safety and enterprise reliability track record that shows up directly in the price. Whether that premium is worth it depends on the workload, which is what the rest of this comparison digs into.
Anthropic was founded in 2021 by Dario Amodei and Daniela Amodei, both former OpenAI researchers who left to build a company organized around what they call Constitutional AI, a training approach meant to bake safety constraints into the model rather than bolt them on afterward. That founding mission is still visible in how Opus 4.8 is marketed and priced today: Anthropic sells reliability and predictability first, and speed or social-data grounding a distant second.
Grok 4.3 vs Claude Opus 4.8: Full Specs Comparison
Here’s the full Grok vs Claude side-by-side on specs, pricing, and benchmark scores, pulled from vendor documentation and the independent trackers cited above.
| Spec | Grok 4.3 (xAI) | Claude Opus 4.8 (Anthropic) |
|---|---|---|
| Developer | xAI | Anthropic |
| Release date | April 30, 2026 (full API) | May 28, 2026 |
| API input price | $1.25 per 1M tokens | $5.00 per 1M tokens |
| API output price | $2.50 per 1M tokens | $25.00 per 1M tokens |
| Context window | 1M tokens | 1M tokens |
| Max output tokens | Not officially published | 128,000 tokens |
| Artificial Analysis Intelligence Index | 53 | 61.4 |
| GPQA Diamond | 90.1% | Not independently published for 4.8 |
| SWE-bench Verified | Not published by xAI | 88.6% |
| SWE-bench Pro | Not published | 69.2% |
| Output speed | 109.1 tokens/second | Not published in sources reviewed |
| Time to first token | 24.03 seconds (Artificial Analysis test) | Not published in sources reviewed |
| Native video input | Yes | No |
| Consumer subscription | SuperGrok $30/mo, Heavy $300/mo | Not covered in this comparison |
Two things jump out immediately. First, the context window is a dead heat at 1 million tokens each, so neither model has an edge on how much you can stuff into a single request. Second, Claude Opus 4.8 is the only one of the two with a documented output ceiling, which actually matters for production planning since you know exactly how long a response can run before you need to paginate or chain calls. Grok 4.3’s lack of a published limit is either a non-issue or a landmine depending on how your integration handles unexpectedly long generations.
The ownership structure behind each company also shapes the table above, even though it never shows up as a row. xAI merged with X Corp in 2023, which is the direct reason Grok can read a live social feed that Claude has no equivalent access to. Anthropic, by contrast, has taken investment from both Google and Amazon over the years while staying independent on model training and safety policy, a structure that lines up with its enterprise-first pricing and its heavier emphasis on benchmarks like GDPval-AA that are built around professional, high-stakes tasks rather than social or consumer use cases. Neither structure is inherently better. They just point toward different products, which is exactly what the spec sheet above shows in numbers.
API Pricing Breakdown: Where the 10x Gap Actually Bites
Sticker prices only tell you so much. What actually matters is what a workload costs once you scale it up, and the answer changes a lot depending on whether your use case leans on input tokens (long context, retrieval, chat history) or output tokens (code generation, long-form writing, agentic multi-step responses). The table below runs four realistic monthly volumes through both price sheets.
| Monthly workload | Grok 4.3 cost | Claude Opus 4.8 cost | Cost multiple |
|---|---|---|---|
| Customer-support chatbot: 20M input / 2M output tokens | $30.00 | $150.00 | 5.0x |
| Coding agent: 10M input / 10M output tokens | $37.50 | $300.00 | 8.0x |
| Document/RAG analysis: 100M input / 5M output tokens | $137.50 | $625.00 | 4.5x |
| High-volume content generation: 5M input / 50M output tokens | $131.25 | $1,275.00 | 9.7x |
What a Real Workload Costs Each Month
The multiple isn’t fixed at 10x. It ranges from 4.5x to 9.7x depending on how output-heavy the workload is, and that pattern is completely predictable once you know the base prices. Output tokens carry the full 10x gap ($25 vs $2.50), while input tokens only carry a 4x gap ($5 vs $1.25). A document-heavy RAG pipeline that reads a lot and writes a little sees the smaller 4.5x multiple. A coding agent or content generator that writes far more than it reads sees the gap climb toward the full 10x. Teams building output-heavy agentic pipelines are exactly the ones who feel Claude’s premium hardest, and exactly the ones who should benchmark quality per dollar before committing to either vendor at scale.
Stretch the coding-agent scenario (10M input, 10M output tokens a month) out to a full year and the gap compounds into a real budget line. At that pace, Grok 4.3 runs $2,100.00 a year, while Claude Opus 4.8 runs $12,000.00 for the identical volume, a difference of $9,900.00 annually for one moderate-traffic agent. Multiply that by a dozen internal tools or a customer-facing product with real usage, and the model choice stops being an engineering decision and becomes a line item a finance team will ask about directly.
Benchmark Performance: Intelligence Index, GPQA, and SWE-bench
No single benchmark settles an argument like this, so here are the scores from three separate source families: Artificial Analysis’s own composite index, independent GPQA aggregators, and SWE-bench trackers that follow the Princeton NLP/OpenAI SWE-bench methodology.
| Benchmark | Grok 4.3 | Claude Opus 4.8 | Source |
|---|---|---|---|
| Artificial Analysis Intelligence Index | 53 | 61.4 | Artificial Analysis, mid-June 2026 snapshot |
| GPQA Diamond | 90.1% | Not independently published for 4.8 | CoderSera, BenchLM, and other model trackers |
| SWE-bench Verified | Not published by xAI | 88.6% | Independent SWE-bench trackers |
| SWE-bench Pro | Not published | 69.2% | Independent SWE-bench trackers |
| GDPval-AA (economic tasks) | Not published | 1,890 Elo (category leader) | Anthropic benchmark reporting |
Claude Opus 4.8 leads on every benchmark that both models actually report, which is a real result, not spin. But the gaps on GPQA Diamond and SWE-bench aren’t apples-to-apples comparisons since xAI simply hasn’t released matching scores for Grok 4.3 on those two tests. Grok’s 88.6% on SWE-bench Verified, where the published number is strong on its own terms. Treat any “Claude wins across the board” framing with a little skepticism until xAI closes the reporting gap on coding benchmarks specifically.
It helps to know what these tests actually measure. GPQA Diamond is a set of graduate-level science questions in biology, physics, and chemistry, written specifically to resist quick web lookups, so a high score reflects genuine reasoning rather than search skill. SWE-bench Verified and SWE-bench Pro pull real, human-confirmed GitHub issues and check whether a model’s patch actually resolves them in a live repository, which is a much harder bar than a multiple-choice coding quiz. GDPval-AA, the benchmark where Claude Opus 4.8 posted its 1,890 Elo score, is built around economically valuable, multi-step professional tasks rather than isolated puzzles, which is why Anthropic leans on it so heavily in its own marketing.
Price-Performance: Cost Per Intelligence Point
Raw benchmark scores don’t account for what you paid to get them, so it’s worth translating the Intelligence Index numbers into a cost-per-point figure using the 100-million-input, 20-million-output workload from the pricing table above. At that volume, Grok 4.3 costs $175 to produce a model that scores 53 on the Index, which works out to $3.30 per Intelligence Index point. Claude Opus 4.8 costs $1,000 at the same volume for a score of 61.4, or $16.29 per point.
| Metric | Grok 4.3 | Claude Opus 4.8 |
|---|---|---|
| Intelligence Index score | 53 | 61.4 |
| Cost at 100M input / 20M output tokens | $175.00 | $1,000.00 |
| Cost per Intelligence Index point | $3.30 | $16.29 |
Claude Opus 4.8 scores about 15.8% higher on the Index, but it costs roughly 4.9 times more per point delivered at this volume. That’s the cleanest way to see the actual trade being offered: a modest, real capability edge for Claude, purchased at a price that scales much faster than the capability gap itself. Whether that trade is worth it depends entirely on whether the extra reliability shows up where it counts for your specific workload, which is exactly the question the use-case section below tries to answer.
Speed and Latency: Throughput vs Response Time
Grok 4.3 measured 109.1 output tokens per second in Artificial Analysis testing, well ahead of the roughly 71 tokens per second category average for comparable models. That’s a real advantage once generation starts, especially for long-form output where every extra token adds latency for the end user.
The catch is time to first token. In the same testing, Grok 4.3 took 24.03 seconds before it started streaming a response, a number that will matter a lot for interactive, chat-style applications and much less for batch or asynchronous workloads. Anthropic hasn’t published comparable throughput or latency figures for Opus 4.8 in the sources reviewed for this piece, so teams evaluating response time for a specific product should benchmark both APIs directly against their own prompts rather than lean on vendor claims from either side.
Grok 4.3 also runs verbose. Artificial Analysis recorded 83 million output tokens generated during its full Intelligence Index evaluation run, compared with a 63 million token median across the models it tracks. That’s useful context for the pricing math earlier in this piece: a model that tends to write longer answers pushes real-world costs closer to the output-heavy end of the cost table, not the input-heavy end, even in tasks that don’t look output-heavy on paper.
Real-Time Data Access vs Enterprise Reliability
The two models are positioned around genuinely different strengths. Grok’s connection to X gives it a grounding in live social and news context that a purely training-data-bound model can’t replicate without a separate retrieval layer bolted on. For anything touching current events, trending topics, or public sentiment, that’s a structural advantage rather than a marketing claim.
Claude Opus 4.8 leans the other way. Anthropic’s pitch, backed by its SWE-bench and GDPval-AA scores, is reliability over long, autonomous task runs and in regulated or enterprise settings where a wrong answer costs more than a slow one. Neither positioning is objectively “better.” A brand-monitoring startup and a bank’s internal compliance tool have almost nothing in common in terms of what they need from a model, and that’s exactly why this comparison keeps circling back to workload-specific math instead of a single overall winner.
The split shows up in how each company talks about its own product, too. xAI markets Grok around speed, cost, and being plugged into what’s happening right now, while Anthropic markets Opus 4.8 around trust: predictable behavior, a documented output ceiling, and benchmark scores pulled from tasks meant to resemble real professional work rather than academic test sets. Buyers who already know which of those two pitches matches their own product usually don’t need a benchmark table to make the call. The table is for everyone in between.
Coding and Agentic Workflows
For coding specifically, the published data points one direction. Claude Opus 4.8’s 88.6% on SWE-bench Verified and 69.2% on SWE-bench Pro.2% on SWE-bench Pro are strong scores against a benchmark built from real, verified GitHub issues rather than synthetic test problems. Its GDPval-AA lead (1,890 Elo) adds evidence that it holds up on economically valuable, multi-step tasks, not just isolated coding puzzles.
Grok 4.3 doesn’t have a public SWE-bench score to set against that, which is a gap in xAI’s own reporting rather than proof the model is weak at code. What Grok does bring to agentic workloads is cost. An agent that fires off dozens or hundreds of tool calls per task racks up tokens fast, and at $2.50 per million output tokens versus $25, the economics favor Grok heavily for high-volume, low-supervision agent loops where a few extra retries are cheaper than Anthropic’s per-call premium. Teams building coding agents that need the highest possible first-pass accuracy still have reason to pay up for Claude. Teams running high-volume, self-correcting agent swarms have real reasons to at least benchmark Grok before ruling it out.
There’s also a practical middle path worth testing before committing fully to either vendor: route the planning and review steps of an agent pipeline to Claude Opus 4.8, where a mistake is expensive, and route the high-volume, lower-stakes execution steps to Grok 4.3, where a retry costs a fraction of a cent. That kind of split-model architecture is more engineering work up front, but it’s the only approach that captures both Claude’s reliability edge and Grok’s cost advantage in the same pipeline instead of forcing a single vendor to cover every step.
5 Real-World Use Cases for Grok 4.3 and Claude Opus 4.8
Specs and benchmarks matter less than how they play out against an actual product. Here are five scenarios where the numbers above point toward one model over the other.
- Social listening and brand monitoring tools. A team building a dashboard that tracks live sentiment around a brand, product launch, or news event benefits directly from Grok 4.3’s connection to real-time X data, something Claude Opus 4.8 can’t replicate without an external retrieval pipeline.
- High-volume customer support triage. A support platform routing and drafting first-pass replies to tens of thousands of tickets a month cares more about the $2.50-per-million-token output price than the last few points of benchmark accuracy, making Grok 4.3 the cheaper default for first-line triage.
- Autonomous coding agents for internal tooling. A team building an agent that opens pull requests, refactors code, or fixes CI failures unattended leans toward Claude Opus 4.8’s 88.6% SWE-bench Verified score, where a wrong or broken change costs far more than the extra $22.50 per million output tokens.
- Regulated-industry document review. Legal, healthcare, and financial services workflows that require the most defensible, enterprise-grade reliability track record tend to favor Claude Opus 4.8, where Anthropic’s safety-focused positioning and GDPval-AA lead carry more weight than raw cost per call.
- Video and multimodal content pipelines. A product that needs to reason over video clips directly, rather than pre-processed transcripts or frame captures, has a real reason to pick Grok 4.3 for its native video input support, which Claude Opus 4.8 doesn’t offer.
- Bootstrapped startups and indie developers. A small team building an MVP on a tight token budget can run roughly 5 to 10 times more volume on Grok 4.3 for the same spend, buying runway to iterate on the product before revenue justifies Claude’s premium.
- Long, autonomous multi-step agent runs. A workflow that has to plan, execute, and self-correct across dozens of chained steps without a human checking each one benefits from Claude Opus 4.8’s GDPval-AA lead, since a single dropped step deep into a long run is far more expensive to debug than a higher per-call price.
Laid out as a quick reference, the recommendation changes depending on what you’re optimizing for:
| If you need… | Choose | Why |
|---|---|---|
| Lowest possible cost per token | Grok 4.3 | 4x cheaper input, 10x cheaper output |
| Highest coding reliability | Claude Opus 4.8 | 88.6% SWE-bench Verified, no published Grok equivalent |
| Live social or news context | Grok 4.3 | Native connection to X data |
| Predictable output length for pagination | Claude Opus 4.8 | Documented 128,000-token output cap |
| Native video understanding | Grok 4.3 | Supports video input directly |
| Enterprise/regulated-industry defensibility | Claude Opus 4.8 | Stronger published safety and reliability track record |
| Fastest raw output speed | Grok 4.3 | 109.1 tokens/second vs a 71 tok/s category average |
The Pace of Change: How Long This Comparison Will Stay Accurate
Every number in this article reflects publicly available data as of June 23, 2026, and that shelf life is shorter than most buyers assume. Independent tracking pages were already listing a newer Grok model in xAI’s lineup while this piece was being reported, a reminder that frontier-model rankings move in weeks, not years. Anthropic and xAI have both shipped a new flagship roughly every one to three months over the past year, and pricing has moved almost as often as the benchmark scores.
None of that makes this comparison useless. It means the underlying pattern, that xAI competes on price and speed while Anthropic competes on reliability and coding performance, is more durable than any single price point or Intelligence Index score. Before committing budget based on the exact dollar figures above, check Anthropic’s live pricing page and Grok 4.3’s OpenRouter listing directly, since either could shift again before this article’s next update.
Migration Guide: Switching Between Grok 4.3 and Claude Opus 4.8
Both APIs use a familiar chat-completion request shape, so switching is more about re-tuning prompts and re-checking cost assumptions than rewriting your whole integration. Here’s what to check in each direction.
Moving From Claude Opus 4.8 to Grok 4.3
- Recalculate your budget before you celebrate the lower sticker price. The savings are real (4x on input, 10x on output) but only show up if your actual usage pattern matches the estimate.
- Add your own output-length guardrails. Claude’s 128,000-token output cap gives you a predictable ceiling, while Grok 4.3 doesn’t document an equivalent limit, so build truncation or max-token parameters into your own request configuration.
- Re-test coding and agentic tasks specifically. Without a published SWE-bench score for Grok 4.3, don’t assume parity with Opus 4.8 on code-heavy workloads. Run your own eval set before cutting over anything customer-facing.
- Expect faster raw throughput (109.1 tokens/second) but budget extra time for the first token, which ran 24.03 seconds in independent testing.
Moving From Grok 4.3 to Claude Opus 4.8
- Model your new costs against the workload table above before migrating, not after. Output-heavy pipelines will feel close to the full 10x gap, not the smaller 4x-5x range that input-heavy workloads see.
- Take advantage of the documented 128,000 max output token limit to simplify pagination logic that may have been built defensively around Grok’s undocumented ceiling.
- Drop any real-time X/social data dependency from your prompt design and replace it with an explicit retrieval step, since Claude Opus 4.8 has no equivalent built-in live data connection.
- Lean into agentic and coding workloads first. That’s where Claude’s 88.6% SWE-bench Verified score and GDPval-AA lead deliver the clearest return on the higher price.
Both vendors use a similar JSON request pattern, so the actual code change is usually small. A minimal example of the structural difference looks like this:
# Grok 4.3 (xAI API)
POST https://api.x.ai/v1/chat/completions
{
"model": "grok-4.3",
"messages": [{"role": "user", "content": "Summarize this document."}],
"max_tokens": 4096
}
# Claude Opus 4.8 (Anthropic API)
POST https://api.anthropic.com/v1/messages
{
"model": "claude-opus-4-8",
"max_tokens": 4096,
"messages": [{"role": "user", "content": "Summarize this document."}]
}The parameter names and endpoint paths differ, but the mental model is the same. Most of the migration work goes into re-testing prompts, re-checking output length assumptions, and rebuilding cost dashboards around the new per-token math, not into rewriting core application logic.
Testing Checklist Before You Cut Over
Whichever direction you’re migrating, run through the same short checklist before you flip production traffic. Skipping it is how teams end up with a surprise bill or a broken output parser in week one.
- Run your actual prompt set against both APIs and compare output quality side by side, not just benchmark scores from a table.
- Log real input and output token counts from a week of production traffic, then plug those numbers into the cost table above instead of guessing your workload mix.
- Check how your code handles a response that gets cut off mid-generation, since the two models cap output length very differently.
- Confirm your retry and rate-limit handling against each vendor’s actual API behavior rather than assuming they match.
- Re-run any safety or content-policy tests specific to your product, since the two vendors take different approaches to what they will and won’t generate.
Grok 4.3 Pros and Cons
Pros:
- Dramatically cheaper API pricing: 4x lower on input tokens, 10x lower on output tokens versus Claude Opus 4.8
- Faster raw output throughput at 109.1 tokens per second, ahead of the category average
- Real-time X data grounding that closed rivals can’t match without extra infrastructure
- Native video input support, unusual among closed frontier models
- Strong 90.1% GPQA Diamond score and a matching 1M token context window
Cons:
- No published SWE-bench Verified score, making coding reliability harder to evaluate up front
- High 24.03-second time-to-first-token in independent testing
- No officially documented max-output-token ceiling, adding integration uncertainty
- Lower Artificial Analysis Intelligence Index score than Claude Opus 4.8 (53 vs 61.4)
Claude Opus 4.8 Pros and Cons
Pros:
- Leads on SWE-bench Verified (88.6%) and SWE-bench Pro (69.2%) for agentic coding work
- Category-leading GDPval-AA score (1,890 Elo) on real-world economic tasks
- Highest Artificial Analysis Intelligence Index score tracked that week (61.4)
- Documented 128,000 max output token ceiling for predictable integration planning
- Stronger published track record on enterprise safety and reliability
Cons:
- Costs 4x to 10x more per token depending on the input/output mix of your workload
- No built-in real-time social or web data grounding
- No native video input support
- Higher cost makes high-volume, low-margin use cases much harder to justify
Which Should You Choose? The Verdict
Grok vs Claude isn’t a contest with one champion. Neither model wins outright, and the data above says so pretty clearly. Grok 4.3 wins on price at every volume tested, from a 4.5x advantage on input-heavy document workloads up to a 9.7x advantage on output-heavy content generation. It also wins on raw output speed and on any use case that benefits from live social or news context, plus native video input. Claude Opus 4.8 wins on every benchmark both vendors actually publish: an Intelligence Index of 61.4 versus 53, an 88.6% SWE-bench Verified score with no published Grok equivalent to compare against, and the GDPval-AA lead at 1,890 Elo.
The practical takeaway: pick Grok 4.3 by default for cost-sensitive, high-volume workloads, real-time or social-data-adjacent products, and anything where a 24-second time-to-first-token is tolerable. Pick Claude Opus 4.8 when the task is agentic coding, long autonomous runs, or regulated work where the published reliability numbers justify paying 4 to 10 times more per token. Teams running mixed workloads increasingly route by task, sending cheap, high-volume, socially grounded work to Grok 4.3 and routing the coding-heavy, reliability-critical slice to Claude Opus 4.8. That split, not a single winner, is what the numbers in this comparison actually support.
If you only take one number away from this comparison, make it the $3.30-versus-$16.29 cost-per-Intelligence-Index-point gap from earlier in this piece. It captures both halves of the story in a single figure: Claude Opus 4.8 really is the stronger model on the benchmarks both vendors publish, and Grok 4.3 really is the cheaper way to buy intelligence per dollar. Neither fact cancels the other out, and a team that picks based on price alone or benchmark rank alone is optimizing for half the equation. The teams getting the most value in mid-2026 are the ones treating this as a routing decision made per workload, not a single vendor contract signed once and left alone.
Frequently Asked Questions
Is Grok 4.3 cheaper than Claude Opus 4.8?
Yes. Grok 4.3 costs $1.25 per million input tokens and $2.50 per million output tokens, while Claude Opus 4.8 costs $5.00 and $25.00 for the same two categories. That’s a 4x gap on input tokens and a 10x gap on output tokens.
Which model scores higher on coding benchmarks?
Claude Opus 4.8, on every published number. It posts 88.6% on SWE-bench Verified and 69.2% on SWE-bench Pro.2% on SWE-bench Pro. xAI has not released a SWE-bench Verified score for Grok 4.3, so a full side-by-side isn’t possible with public data yet.
Do Grok 4.3 and Claude Opus 4.8 have the same context window?
Yes, both ship a 1 million token context window. Claude Opus 4.8 also publishes a hard cap of 128,000 max output tokens per request, a limit xAI hasn’t clearly documented for Grok 4.3.
Can Grok 4.3 pull real-time information from X?
xAI has built Grok’s positioning around real-time, socially grounded reasoning, and it’s a major reason developers pick it over closed rivals for monitoring, news-adjacent, or sentiment-tracking tools.
Is Claude Opus 4.8 worth the higher price for enterprise use?
For agentic coding, long autonomous task runs, and workloads where Anthropic’s enterprise safety and reliability track record matters more than raw cost, the benchmark evidence supports paying the premium. For high-volume, cost-sensitive workloads, it’s a much harder case to make.
What is SuperGrok, and do I need it to use the Grok 4.3 API?
SuperGrok ($30/month) and SuperGrok Heavy ($300/month) are consumer subscriptions for xAI’s chat app, not a requirement for API access. Developers pay per token directly through the API, separate from consumer subscription pricing.
Which model is faster?
Grok 4.3 posted 109.1 output tokens per second in Artificial Analysis testing, ahead of the roughly 71 tokens per second category average, though its time-to-first-token in the same testing ran 24.03 seconds. Anthropic hasn’t published comparable throughput figures for Opus 4.8, so benchmark both against your own prompts before deciding.
Is either model open source?
No. Both Grok 4.3 and Claude Opus 4.8 are closed, proprietary models available only through their respective vendor APIs.
How much would switching from Claude Opus 4.8 to Grok 4.3 save a coding agent workload?
At 10 million input and 10 million output tokens a month, a typical coding-agent volume, Grok 4.3 costs $37.50 versus $300.00 for Claude Opus 4.8, an 8x difference. Scaled to a full year, that’s roughly $450 versus $3,600 for the same workload.
Does Grok 4.3 support image and video input?
Yes. Grok 4.3 accepts text and image input and, according to xAI’s model documentation, native video input as well, a combination Claude Opus 4.8 does not match since it has no native video understanding.
Where does Grok 4.3 rank against other AI models overall?
On Artificial Analysis’s broader leaderboard, which tracks well over 100 models, Grok 4.3 has ranked in the middle of the pack rather than at the very top. Rankings shift constantly as new models launch, so treat any single rank as a snapshot rather than a permanent verdict, and check the live leaderboard before making a purchasing decision based on rank alone.
Can I use both models in the same product?
Yes, and a growing number of teams do exactly that. A common pattern routes cheap, high-volume, or real-time-data tasks to Grok 4.3 while sending coding, planning, or other reliability-critical steps to Claude Opus 4.8, capturing the cost advantage of one model and the benchmark lead of the other in a single pipeline.
Related Coverage
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