Claude Opus 5.5 has taken the top spot on the Epoch Capabilities Index, the composite AI benchmark run by independent research group Epoch AI, edging out OpenAI’s GPT-6 Astra by less than a single point. The index, which stitches together more than 50 separate evaluations into one score, put Anthropic’s flagship model at roughly 167 points as of October 11, 2026, with GPT-6 Astra trailing at around 166.4 to 166.5, according to a leaderboard snapshot published by benchmark tracker Orca Router.
The gap sounds trivial, and in statistical terms it is. But the ranking shuffle lands in the middle of the busiest model-release stretch of 2026, with Mistral opening public preview access to Mistral Large 4 on October 6, Google shipping Gemini updates, and a wave of Chinese open-weight models climbing a separate leaderboard built by Artificial Analysis. Taken together, the numbers show two things happening at once: the gap between the best closed labs has nearly disappeared, and the gap between closed and open models is shrinking just as fast.
What the Epoch Capabilities Index Actually Measures
Epoch AI built the Epoch Capabilities Index, often shortened to ECI, as an attempt to avoid the trap that swallows most single-benchmark leaderboards: a model can top one math test or one coding suite and still be mediocre everywhere else. The ECI instead aggregates results across more than 50 evaluations covering reasoning, coding, science, and general knowledge, then rescales the composite so that Claude 3.5 Sonnet, an older Anthropic model, sits at a baseline score of 130. Every model released since is measured against that fixed point, which lets Epoch AI track capability growth across labs and years rather than just across a single quarter, as described on the organization’s own benchmarks page.
That scaling choice explains why a score difference of under one point actually represents a meaningful compression of the field. When Claude 3.5 Sonnet sat at 130, the spread between the best and worst frontier models was often 15 to 20 points. By October 2026, the top two labs are separated by less than a full point, and the next tier down isn’t far behind either.
The Numbers Behind the 0.9-Point Lead
Epoch AI’s own October 1 announcement rounded Claude Opus 5.5’s score to 167 and described the model as narrowly ahead of GPT-6 Astra. More granular readings diverge slightly depending on the snapshot date: a leaderboard capture from October 2 put Claude Opus 5.5 at 167.35 against GPT-6 Astra’s 166.51, a gap of 0.84 points, while the October 11 snapshot from Orca Router listed 167.3 versus 166.4, a gap closer to 0.9 points. Either way, the lead sits comfortably under one full point on a scale where differences of five or more points used to separate the top model from its nearest rival.
Orca Router’s analysis also flagged something that matters more than the headline number: Epoch AI’s published 95 percent confidence intervals for the two models overlap heavily. Claude Opus 5.5’s interval runs from roughly 164.0 to 172.0, and GPT-6 Astra’s runs from about 163.1 to 171.1. Statistically, that overlap means the win is real in the point estimate but not decisive in the uncertainty range Epoch AI itself publishes alongside the score. Anthropic and OpenAI are, by the most rigorous public measure available, in a dead heat.
The same Epoch AI update noted that Claude Sonnet 5.5, Anthropic’s mid-tier model, had roughly matched Claude Fable 5.1 at a score of 165, putting two Anthropic models within two points of the overall leader. Readers who want the pricing side of that story can see our Claude Sonnet 5.5 API setup walkthrough, which breaks down the per-token costs behind that tier.
| Model | Developer | Epoch Capabilities Index score | Rank | Result date |
|---|---|---|---|---|
| Claude Opus 5.5 | Anthropic | 167.3 | 1 | Sept. 22, 2026 |
| GPT-6 Astra | OpenAI | 166.4 | 2 | Oct. 2026 snapshot |
| Claude Fable 5.1 | Anthropic | 165.0 | 3 (tie) | Oct. 1, 2026 update |
| Claude Sonnet 5.5 | Anthropic | 165.0 | 3 (tie) | Oct. 1, 2026 update |
| Gemini 3.7 Flash | 157.3 | 16 | Aug. 13, 2026 |
Where Google’s Gemini Lineup Lands
Google doesn’t come out of the October 2026 Epoch snapshot looking strong, at least not on this particular index. The data Epoch AI has published lists Gemini 3.7 Flash at 157.3, sitting 16th on the board and roughly ten points behind Claude Opus 5.5. That’s the lightweight Flash tier rather than a top-end Gemini model, so it isn’t a direct apples-to-apples comparison with Opus 5.5 or GPT-6 Astra, both of which are flagship-class releases. Still, the absence of a higher-scoring Gemini entry on the current public leaderboard is notable on its own, especially for a company that has spent 2026 pushing token-context expansions and pricing cuts as its main competitive lever, a strategy covered when Gemini’s 1-million-token tier triggered a free-tier reduction days after launch.
For a fuller side-by-side of Google’s newer releases against OpenAI’s, our breakdown of GPT-6.1 Sol versus Gemini 4 Argon digs into the token-window arms race that’s running in parallel to the capability race measured here.
Mistral Large 4 Enters a Crowded Open-Weight Field
While Anthropic and OpenAI trade fractions of a point at the top of the closed-model leaderboard, the open-weight side of the market picked up a new entrant on October 6, when Mistral opened public preview access to Mistral Large 4. Mistral describes it as the company’s most capable model to date, and early third-party testing backs up at least part of that claim. On Artificial Analysis’s Intelligence Index, a separate scoring system from Epoch’s, Mistral Large 4 Preview landed at 38.4, good enough for roughly eighth place overall and the highest score among models built outside the United States and China, according to a ranking breakdown published by Progressive Robot the same week.
Mistral Large 4 also placed in the top five on what’s known as the AA Cyber Index, a benchmark that scores a model’s ability to find and patch software vulnerabilities, and the company says the model edged out GPT-6 Astra by about 1 percent on Dense200, a benchmark that tests how well a model locates objects of interest inside images rather than pure text reasoning, per coverage from SiliconANGLE. Developers who want to try the model directly can reference our Mistral Large 4 API setup guide for current pricing and endpoint details.
China’s Open Models Keep Climbing the Same Chart
Mistral Large 4’s eighth-place finish on the Artificial Analysis Intelligence Index says as much about how crowded the top of that chart has become as it does about Mistral’s own progress. Chinese labs occupy most of the slots above it. MiMo-V2.6-Pro leads the pack among the models in this comparison set with a score of 46, followed by GLM-5.3 at 45, Kimi K3 at 44, and GLM-5.3-Flash at 42. Alibaba’s Qwen3.8 2.4T and Qwen3.8-Flash-Next both sit at 40, just ahead of DeepSeek V4.1 Flash at 39 and Mistral Large 4 Preview at 38. DeepSeek’s own older Pro tier, DeepSeek V4 Pro, trails at 36.
| Model | Origin | Artificial Analysis Intelligence Index score |
|---|---|---|
| MiMo-V2.6-Pro | China | 46 |
| GLM-5.3 | China | 45 |
| Kimi K3 | China | 44 |
| GLM-5.3-Flash | China | 42 |
| Qwen3.8 2.4T | China | 40 |
| Qwen3.8-Flash-Next | China | 40 |
| DeepSeek V4.1 Flash | China | 39 |
| Mistral Large 4 Preview | France | 38 |
| DeepSeek V4 Pro | China | 36 |
Those nine data points, pulled from the same comparison table, tell a clear story: the open-weight race is no longer a two-horse contest between DeepSeek and Mistral. It’s a crowded field with at least six labs scoring within ten points of each other, most of them Chinese. Readers tracking the smaller, cheaper end of that market can see how the fast-tier models stack up in our comparison of DeepSeek V4.1 Flash, Claude Haiku 5.5, and Gemini 3.8 Flash.
Reflection AI’s Beam and the Limits of Cross-Index Comparisons
Reflection AI’s open-weight Beam model doesn’t appear with an overall score on the Artificial Analysis Intelligence Index in the data currently available, which makes it hard to slot into the table above. What is public is narrower: Beam scored 44.4 on a coding benchmark called DeepSWE v1.1 and 37.0 on AutomationBench, a test of task-automation ability. Those are benchmark-specific figures, not an aggregate intelligence score, and comparing them directly to the Epoch or Artificial Analysis composite numbers above would be misleading. It’s a reminder that the AI benchmarking world currently runs on at least two incompatible scoring systems, Epoch’s Capabilities Index and Artificial Analysis’s Intelligence Index, with different evaluation sets, different scaling, and no agreed conversion between them.
Historical Context: A Leaderboard That Resets Every Few Weeks
It helps to remember how fast this leaderboard turns over. A year earlier, Claude Haiku’s predecessor scored roughly 17 points lower on the Artificial Analysis Intelligence Index than the current Claude Haiku 5.5, which now sits at 43 on that scale, a 26-point jump in a single generation. Anthropic’s price moves have tracked that capability jump directly: the company cut Haiku pricing by 75 percent when it launched Haiku 5.5, and followed with API pricing for the Opus and Sonnet tiers that undercut where the previous generation started.
That pattern, bigger capability jumps paired with falling per-token prices, has repeated across every major lab in 2026. GPT-6’s API pricing dropped by half even as OpenAI expanded its interface features. Google trimmed its free Gemini tier days after extending context windows to a million tokens. Mistral opened Large 4 in public preview at a published rate near $4.18 per million tokens, competitive with the mid-tier pricing Anthropic and OpenAI now charge for their second-string models. The net effect for buyers is that the best model keeps changing, sometimes week to week, which has made the Epoch and Artificial Analysis leaderboards into de facto purchasing guides for engineering teams that used to just default to whichever lab they signed with first.
Why a Sub-1-Point Lead Still Matters for Enterprise Buyers
A 0.9-point gap on a 167-point scale sounds like noise, and statistically Orca Router’s overlapping confidence intervals back that reading up. But procurement teams rarely buy on raw capability scores alone. They buy on capability plus price plus context window plus integration cost, and a headline claim of being the best model in the world still moves enterprise sales conversations even when the underlying margin is a fraction of a point. Anthropic’s marketing has leaned on the Epoch result since the October 1 announcement, and OpenAI is likely to do the same the moment GPT-6 Astra retakes the top spot, which given the overlapping intervals could happen on the next evaluation cycle without any new model shipping at all.
That volatility is exactly why procurement teams increasingly build routing logic that calls different models for different tasks rather than standardizing on one vendor. Teams running agentic workflows have been doing this for months already; see our guide to setting up Claude Opus 5.5 through the API for the mechanics of swapping a top-tier model into an existing pipeline.
The Open-Closed Gap Is Shrinking Faster Than Expected
Perhaps the more consequential trend sitting underneath this week’s leaderboard shuffle is the one Mozilla flagged in a September 2026 report: the strongest open-weight models now trail the leading closed models by roughly three points on capability, while costing about 60 percent as much to run. Three points on the Artificial Analysis scale, where the gap between first and ninth place among the models in the table above spans just ten points, is a thin margin. If that trend holds, the practical argument for paying a premium for a closed frontier model gets weaker every quarter, particularly for enterprise workloads that don’t need the absolute top score, just a score that’s good enough at a fraction of the cost.
What This Means for Anthropic, OpenAI, Google, Mistral and DeepSeek
For Anthropic, the Epoch result is a genuine marketing win, the first time the company has held the top slot on a major independent composite index since the Claude 3.5 era, and it arrives while Anthropic is also fielding reports that large customers are trimming their Claude usage in favor of in-house tooling, a tension between benchmark leadership and commercial retention that the company will need to manage carefully heading into 2027.
For OpenAI, finishing within a statistical rounding error of first place on the ECI while GPT-6 Astra also posted a strong Dense200 multimodal score, losing to Mistral Large 4 by only about 1 percent, suggests the company’s multimodal investments are paying off even where its pure text-reasoning lead has narrowed.
For Google, the absence of a flagship-tier Gemini score near the top of the current public Epoch snapshot is a gap worth watching, especially given how aggressively the company has marketed context-window size as its main differentiator this year.
For Mistral and the Chinese open-weight labs, cracking the top ten of a widely cited intelligence index while charging a fraction of frontier-model prices validates a strategy that treats good enough and much cheaper as a direct competitive weapon against the top two closed labs, rather than a consolation prize for smaller players.
Predictions: Where the Leaderboard Goes Next
- Expect GPT-6 Astra or a successor variant to retake the Epoch Capabilities Index top spot within weeks, given that the current gap sits inside the published confidence intervals for both models.
- Google will likely respond with a flagship-tier Gemini release built specifically to compete on composite indexes rather than context-window size alone, after watching Gemini 3.7 Flash land well outside the top ten.
- At least one more Chinese open-weight lab, most plausibly a newer MiMo or GLM release, will crack the top five of the Artificial Analysis Intelligence Index before the end of 2026, continuing the trend visible in the current nine-model comparison.
- Pricing pressure will keep compounding with capability gains: expect further API price cuts from Anthropic, OpenAI, and Mistral in the fourth quarter of 2026 as each lab tries to make its benchmark position matter commercially, not just reputationally.
- The gap between Epoch’s and Artificial Analysis’s scoring systems will keep causing confusion in public comparisons until one of the two organizations publishes a cross-index conversion methodology, something neither has done as of this report.
How the Indexes Differ: A Quick Reference
Readers comparing scores across this article and other coverage should keep the two main systems straight. The Epoch Capabilities Index rescales everything against a Claude 3.5 Sonnet baseline of 130 and aggregates more than 50 evaluations weighted toward reasoning and general capability. The Artificial Analysis Intelligence Index uses a different baseline and a different evaluation mix, which is why Claude Opus 5.5’s Epoch score of 167 and Mistral Large 4’s Artificial Analysis score of 38 are not comparable numbers, despite both measuring intelligence in a loose sense. Treat each leaderboard as internally consistent but not interchangeable with the other.
Why Rankings Shift Week to Week
Both Epoch AI and Artificial Analysis re-run evaluations as new model versions, including preview and point releases, become available through public APIs. That means a lab doesn’t need to announce a full new model generation to move the needle. A quiet point release, like the Mistral Large 4 preview that debuted October 6, can reshuffle several rows of a leaderboard within days, which is exactly what happened this month.
What Buyers Should Actually Do With This Data
Engineering leads evaluating which model to standardize on should weight the confidence intervals, not just the headline rank, and should separately evaluate price per token, context window, and task-specific benchmarks like DeepSWE or AutomationBench scores rather than relying on a single composite number. A model that’s third on the Epoch index but costs a third as much, like several of the open-weight entries above, may be the better procurement decision for most workloads even though it isn’t the leaderboard winner.
Frequently Asked Questions
What is the Epoch Capabilities Index?
It’s a composite AI benchmark built by Epoch AI that aggregates more than 50 individual evaluations into a single score, rescaled so that Claude 3.5 Sonnet equals 130, letting researchers track capability growth across model generations.
How much better is Claude Opus 5.5 than GPT-6 Astra?
Depending on the snapshot date, Claude Opus 5.5 scores between 167.3 and 167.35 on the Epoch Capabilities Index, while GPT-6 Astra scores between 166.4 and 166.51, a gap of roughly 0.84 to 0.9 points. Their published confidence intervals overlap substantially.
Is this ranking statistically significant?
Not decisively. Epoch AI’s own 95 percent confidence intervals for Claude Opus 5.5, roughly 164.0 to 172.0, and GPT-6 Astra, roughly 163.1 to 171.1, overlap, meaning the point-estimate lead could reverse on the next evaluation cycle without a new model release.
Where does Google’s Gemini rank?
The most recent public Epoch snapshot lists Gemini 3.7 Flash at 157.3, ranked 16th, roughly ten points behind Claude Opus 5.5. That’s a lightweight Flash-tier model rather than Google’s top-end offering.
How does Mistral Large 4 compare to the leaders?
Mistral Large 4 Preview, which opened to public preview on October 6, 2026, scored 38.4 on the Artificial Analysis Intelligence Index, placing roughly eighth overall and first among non-US, non-China models. It is not directly comparable to Epoch’s scores since the two indexes use different scales.
Are open-weight models catching up to closed models?
A September 2026 Mozilla report found the strongest open models trail the leading closed models by about three points on capability while costing roughly 60 percent as much to run, suggesting the gap is narrowing faster than price-performance expectations from a year earlier.
What about DeepSeek and other Chinese models?
Several Chinese labs now outscore Mistral Large 4 on the Artificial Analysis Intelligence Index, including MiMo-V2.6-Pro at 46, GLM-5.3 at 45, Kimi K3 at 44, and GLM-5.3-Flash at 42, with DeepSeek’s V4.1 Flash scoring 39 and its older V4 Pro tier scoring 36.
Will this ranking last?
Unlikely to last long in its exact form. Given the sub-1-point gap and overlapping confidence intervals between Claude Opus 5.5 and GPT-6 Astra, along with the pace of point releases from Mistral, DeepSeek, and others this month, the leaderboard is likely to shift again before the end of October.
