Top 10 AI models in October 2026, ranked by what they do best

TL;DR

• Best overall: Claude Opus 5.5. It leads the Artificial Analysis Intelligence Index v4.3.2 (58 at max effort) and the Epoch Capabilities Index (167), at $4 / $20 per million input / output tokens.
• Best for the hardest reasoning: GPT-6 Astra and Claude Fable 5.1, both at $10 / $50. Best for agentic coding per dollar: Claude Sonnet 5.5 ($2 / $10), which Anthropic reports at 70.6% on Terminal-Bench 4.0.
• Best cost-efficiency: GPT-6.1 Sol, which scored 52 on the index for $0.72 per task. Best multimodal and speed: Gemini 3.8 Flash, which takes text, image, video, audio and PDF input at $0.75 / $3.75 (introductory).
• Best open weights: MiMo-V2.6-Pro (MIT, index 46), then Kimi K3 and DeepSeek V4.1 Flash. Gemini 4 Argon and Claude Mythos 5.1 exist but are restricted, so they are not ranked.
• No single model wins every column. Production teams usually route across several, with failover between providers.

The best AI model in October 2026 depends on the job. A model that leads on graduate-level reasoning can cost fifty times more per output token than one that handles extraction just as well, and the leaderboards disagree with each other at the top. September 2026 alone brought new flagships from Anthropic, OpenAI, Google, Meta, DeepSeek and Xiaomi, so last quarter’s rankings no longer hold. This survey ranks ten models that a developer can call or download today, states the criteria first, and names the best pick for six common jobs: overall reasoning, agentic coding, cost-efficiency, long context, multimodal input and open weights. All figures were read from vendor documentation and independent leaderboards on 5 October 2026. No model was run for this piece.

What changed in September 2026

The frontier turned over in four weeks. Every major Western lab and three Chinese labs shipped a new flagship or a major point release between 2 and 30 September 2026.

• Google released Gemini 3.8 Flash on 2 September and announced Gemini 4 Argon on 30 September.
• Meta shipped Muse Spark 1.3, which Artificial Analysis dates to 2 September and Meta featured at Connect 2026.
• OpenAI released GPT-6 Astra on 3 September, according to Artificial Analysis, followed by the cheaper GPT-6.1 Sol.
• Anthropic released Claude Fable 5.1 and the restricted Claude Mythos 5.1 at the start of the month, Claude Opus 5.5 on 22 September and Claude Sonnet 5.5 on 28 September.
• DeepSeek released V4.1 Flash on 10 September and Xiaomi released the MiMo-V2.6 family under MIT in the second half of the month.

Two patterns stand out. First, the newest flagships are being released in stages. GPT-6 Astra rolled out to trusted enterprises before the wider API, Gemini 4 Argon is limited to cyber defenders, and Claude Mythos 5.1 is available only through Anthropic’s verification programmes. Second, the price of near-frontier capability fell sharply. Opus 5.5 costs 20% less per token than Opus 5, and both OpenAI and Anthropic now sell a second-tier model at $2 / $10 that trails their flagship by a few points.

How the top AI models were chosen

A model qualified for the list only if it met three conditions on 5 October 2026: it exists in an official release post or model card, a developer outside a restricted programme can call it through a public API or download its weights, and at least one independent leaderboard has scored it. That rule excludes Gemini 4 Argon and Claude Mythos 5.1, both covered in a later section.

The criteria below decided the order. They are published before the ranking so a reader can weight them differently.

Two cautions apply to every number in this survey. Vendor-reported benchmarks use the vendor’s own harness and effort settings, so a score from one lab’s post is not directly comparable to the same benchmark in another lab’s post. And aggregate indexes reward breadth, which can hide a model that is the best choice for one narrow task. For a method to check what a release actually discloses, see the guide to reading a model card like an auditor.

Top 10 AI models at a glance

The table ranks the ten models. The Intelligence Index column is the highest-scoring effort setting Artificial Analysis lists for each model; prices are list prices per million tokens for standard, non-batch requests.

Claude Sonnet 5.5 scores higher on the index than GPT-6 Astra and Claude Fable 5.1, yet ranks below Astra. Astra’s lead on tool use, computer use and science, its larger independent footprint, and Sonnet’s higher cost per task at max effort on the same index decided that order; the entries below explain each placement.

Proprietary frontier models

The top two places go to the flagships of Anthropic and OpenAI, and the two labs fill the next three as well. Counting each effort setting as a separate row, the two labs hold 17 of the top 18 rows on the Artificial Analysis leaderboard; the other is the restricted Gemini 4 Argon.

1. Claude Opus 5.5

What it is. Anthropic’s mid-priced flagship, released 22 September 2026 and recommended in Anthropic’s models overview as the starting point “for most workloads”. It uses adaptive thinking that is always on, with a default effort of medium.

Price and availability. $4 per million input tokens and $20 per million output tokens, with cache reads at 5% of the input price and a 50% batch discount. It runs on the Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry and Claude Platform on AWS. Context is 1M tokens with 128K output.

Strengths. It is first on the Artificial Analysis leaderboard at 58 (max effort) and still scores 54 at high effort for $1.82 per index task. Epoch AI lists it as the top model on its Epoch Capabilities Index, at 167. On LMArena’s text board it sits at 1504 at high effort, inside the confidence interval of the top Anthropic entries. Anthropic reports 66.4% on Terminal-Bench 4.0, 81.8% on OSWorld 2.1 and 1846 Elo on GDPval-AA v2.1, ahead of Fable 5.1 on all three.

Limits. Text and image input only, with text output. At max effort an index task cost $5.98, more than GPT-6 Astra at its own max setting. Anthropic’s comparisons with GPT-6 Astra are vendor claims.

Best fit. The default for teams that want one strong model for agents, coding and knowledge work, and the model to test first before paying for Fable 5.1 or Astra.

2. GPT-6 Astra

What it is. OpenAI’s top model, described in its model documentation as “our most capable model for the most demanding work”. It exposes five reasoning effort levels, from low to max, and supports web search, file search, code interpreter, computer use, a hosted shell and MCP tools.

Price and availability. $10 input and $50 output per million tokens, with cached input at $1. Requests over 272K input tokens pay double for input and 1.5 times for output. Context is 1,050,000 tokens with 922,000 maximum input and 128,000 output. OpenAI rolled it out in stages, starting with enterprises in a trusted-access programme, and it is now listed in the public API.

Strengths. It scores 53 on the Artificial Analysis index at max effort for $3.26 per task, cheaper per task than Opus 5.5 or Fable 5.1 at their max settings. Its built-in tool set is the widest of any model here.

Limits. Artificial Analysis measured 60 output tokens per second and a time to first token of about 288 seconds at max effort, because the model reasons for a long time before answering. It was not among the top 25 on LMArena’s text board on 2 October, which may reflect its recent release. Input is text and image only.

Best fit. Research agents, long tool-using tasks and computer use, where quality matters more than latency.

Near-frontier models at lower prices

Places three to five are a cheaper tier and a premium specialist. Two of them cost $2 / $10 per million tokens, a fifth of the flagship rate.

3. Claude Sonnet 5.5

What it is. Anthropic’s speed-and-cost tier, released 28 September 2026. Anthropic says it runs more than 30% faster than Sonnet 5 and needs “far fewer tokens to do the same work”.

Price and availability. $2 input and $10 output per million tokens; cache reads $0.20, cache writes $2.50. Same platforms and the same 1M context and 128K output as Opus 5.5.

Strengths. It scores 56 on the Artificial Analysis index at max effort, second only to Opus 5.5. In Anthropic’s release post it reaches 70.6% on Terminal-Bench 4.0, higher than the 66.4% Anthropic reports for Opus 5.5, and 1844 on GDPval-AA v2.1, two points behind Opus.

Limits. At max effort Sonnet 5.5 cost $7.67 per index task, more than Opus 5.5 at max, because it spends more tokens to reach a similar score. At xhigh effort it scores 52 for $2.75. The lesson is that the per-token price says little until effort is fixed. It is too new to appear in LMArena’s top 25.

Best fit. Coding agents and terminal work where the effort setting is tuned per task, and high-volume use of a near-flagship model.

4. Claude Fable 5.1

What it is. Anthropic’s most capable generally available model, released with Claude Mythos 5.1 in September. According to Anthropic, Fable 5.1 and Mythos 5.1 are the same underlying model with different safeguards; Fable is the general release.

Price and availability. $10 input and $50 output per million tokens, with cache reads at 2.5% of the input price. 1M context and 128K output, on all Claude platforms. Default effort is high.

Strengths. Anthropic reports 60.9% on Humanity’s Last Exam without tools and 65.0% with tools, and 52.6% on Terminal-Bench-Science 0.1, more than double Fable 5. On LMArena’s text board it scores 1501 at max effort. Anthropic’s documentation recommends it “for demanding reasoning and long-horizon agentic work” or when Opus 5.5 at higher effort still falls short.

Limits. Anthropic’s own post for Opus 5.5 shows Opus ahead of Fable 5.1 on Terminal-Bench 4.0, OSWorld 2.1 and GDPval-AA v2.1 at 40% of the price. It scores 53 on the index, five points below Opus 5.5 at max effort, for $7.63 per task.

Best fit. Scientific and research workloads where a team has measured a gain over Opus 5.5 on its own evaluations.

5. GPT-6.1 Sol

What it is. OpenAI’s second-tier model, which its documentation says “delivers near-Astra performance at a lower cost for complex coding, computer use, and professional work”. Effort runs from low to max, with medium as default; there is no none or minimal setting.

Price and availability. $2 input and $10 output per million tokens, one fifth of Astra. Cached input is $0.10. The same 1.05M context, 272K surcharge threshold and 128K output as Astra.

Strengths. It scores 52 on the Artificial Analysis index at max effort, one point below Astra, for $0.72 per task, the lowest cost per task of any model scoring above 50. At high effort it scores 50 for $0.32. On LMArena it is 1483 on text and 1291 on vision.

Limits. Text and image input only. Like Astra, it cannot turn reasoning off, so it is not the cheapest option for simple, high-volume calls.

Best fit. The cost-efficiency pick: production agents and coding workloads that need near-frontier quality at scale.

Fast, multimodal and lower-cost models

The next two models are proprietary but priced well below the flagships, and both accept more input types than any model above them.

6. Gemini 3.8 Flash

What it is. Google’s newest generally available Gemini model, released 2 September 2026 and described in the Gemini API documentation as “engineered for long-horizon software engineering, autonomous agents, and complex enterprise workflows”. It is the third Flash release in six weeks, according to Google.

Price and availability. $0.75 input and $3.75 output per million tokens on introductory pricing through 31 December 2026, rising to $1.50 and $7.50 from 1 January 2027, per Google’s pricing page. Available in the Gemini API, AI Studio, Gemini Enterprise and Google’s own apps. Input limit 1,048,576 tokens, output 65,536.

Strengths. It is the only proprietary model in this list that accepts audio as well as text, image, video and PDF input. Artificial Analysis measured 239 output tokens per second, the fastest here, and an index score of 41 for $1.24 per task. On LMArena it ranks 1495 on text, ahead of every OpenAI model, and 1290 on vision. Google reports 54.9% on HLE-Verified. Thinking modes run from low to high, and it supports computer use in preview, search and Maps grounding, and code execution.

Limits. Its index score is well below the top five. Its output cap is half that of the Claude and GPT models. The introductory price doubles in January, so a cost model built on today’s rate will be wrong next quarter.

Best fit. Video, audio and document understanding, latency-sensitive applications, and high-volume agent steps where a flagship would be overkill.

7. Muse Spark 1.3

What it is. Meta’s proprietary model, released 2 September 2026 and promoted at Connect as bringing “frontier-level reasoning to complex coding and agentic workflows”. It is not open weights.

Price and availability. On the Meta Model API, the standard tier costs $1.25 input and $4.25 output per million tokens, and Meta does not train on standard-tier traffic. A contributor tier costs $0.10 and $0.20 in exchange for permission to train on prompts and completions. It is also on Oracle Cloud, with Google Cloud in private preview. Context is 1M tokens.

Strengths. It scores 48 on the Artificial Analysis index at max effort, above Gemini 3.8 Flash and every open-weight model, at a per-token price below every Anthropic and OpenAI model. LMArena places it at 1494 on text, level with Gemini 3.8 Flash. Artificial Analysis measured 146 output tokens per second. Meta reports 90.3 on OSWorld 2.0 and 88.8 on DeepSWE v1.1.

Limits. Artificial Analysis classes it as “very verbose”: it generated 170M output tokens on the index against a median of 81M, which eats into the per-token saving. Meta’s DeepSWE figure is far above the 77.9% Google claims as a lead for Gemini 4 Argon on the same benchmark version, which shows how much vendor harnesses differ. Teams with data-handling rules should not use the contributor tier.

Best fit. Cost-sensitive agentic and multimodal work where a proprietary API is acceptable but flagship prices are not.

Open-weight models

Open-weight models publish their parameters for download, so a team can run them on its own hardware or pick any hosting provider. The best open models now trail the top closed model by about twelve index points. The licence matters as much as the score, a point mapped in detail in the state of open-weight licensing; a separate ranking of the best open-weight models goes deeper on self-hosting.

8. MiMo-V2.6-Pro

What it is. A sparse mixture-of-experts (MoE) model from Xiaomi with 1.02 trillion total parameters, of which 42 billion are active per token. A mixture-of-experts model routes each token through a small subset of its sub-networks, which keeps inference cost far below what the total size suggests. The model card lists a 1M-token context and native image, video and audio input.

Licence and price. MIT, which permits commercial use with no revenue threshold. Artificial Analysis lists API pricing of $0.43 input and $0.87 output per million tokens.

Strengths. It is the highest-scoring open-weight model on the Artificial Analysis index, at 46, ranked first of 118 open models. Xiaomi reports 71.9 on DeepSWE v1.1, 82.0 on OSWorld-Verified and 34.9 on Terminal-Bench 4.0. Xiaomi also published its reinforcement-learning framework and technical report.

Limits. Artificial Analysis measured 41 output tokens per second, slow for interactive use. A 1-trillion-parameter checkpoint needs a multi-GPU cluster to self-host. It has not yet appeared on LMArena’s text top 25.

Best fit. Teams that need a permissive licence and frontier-adjacent agentic capability, and can host a large MoE model.

Open-weight alternatives

The next two open models trade the top index score for something else: human-preference strength in Kimi K3’s case, and price and speed in DeepSeek V4.1 Flash’s.

9. Kimi K3

What it is. Moonshot AI’s MoE model with 2.8 trillion total parameters and 104 billion active, routing each token to 16 of 896 experts. Artificial Analysis dates its API launch to 16 July 2026; the weights on Hugging Face followed. Input covers text, image and video.

Licence and price. The custom Kimi K3 License, which Artificial Analysis classes as allowing commercial use with restrictions. On Moonshot’s API, $3 input ($0.30 cached) and $15 output per million tokens, with a 1,048,576-token context.

Strengths. It is the highest-placed open-weight model on LMArena’s text board, at 1488, and scores 44 on the Artificial Analysis index. Moonshot reports 93.5 on GPQA Diamond and 91.2 on BrowseComp.

Limits. Its hosted price is higher than Muse Spark 1.3 and close to GPT-6.1 Sol, which both score higher on the index. Artificial Analysis measured 45 tokens per second. The model is large to self-host, and the licence needs a legal read before commercial deployment.

Best fit. Self-hosted multimodal agents and browsing tasks where an open model with strong human-preference scores matters.

10. DeepSeek V4.1 Flash

What it is. A 552-billion-parameter MoE model from DeepSeek, released 10 September 2026, with about 16 billion parameters active during decoding. The model card describes a compressed attention design that cuts the memory each token takes in the cache to about a quarter of V4 Flash, which matters for long agent sessions. Input is text and image.

Licence and price. MIT. DeepSeek’s API pricing page lists its deepseek-flash model at $0.30 input and $1.20 output per million tokens at peak hours, half that off-peak, with cache hits from $0.003. Context is 1M tokens with up to 384K output.

Strengths. It cost $0.27 per Artificial Analysis index task, the lowest of any model here, and ran at 213 output tokens per second. It scores 39 on the index. DeepSeek reports 74.2% on DeepSWE v1.1 and 90.9% on GPQA Diamond at max reasoning effort.

Limits. Nineteen index points behind the leader. Artificial Analysis notes high verbosity (250M tokens against a 140M median). DeepSeek’s off-peak discount depends on time of day in UTC, which complicates cost forecasting.

Best fit. High-volume extraction, classification and coding assistance where price and speed beat peak capability, hosted or self-hosted.

Best AI model for each job

Most readers need one answer per job, not one overall winner. The table maps six common jobs to the first model to evaluate and the runner-up.

Long context deserves a caveat. Every model in the top seven now advertises roughly 1M tokens, so the window alone no longer separates them. Price beyond a threshold and recall quality do. OpenAI charges double for input beyond 272K tokens, and none of the vendors in this list publishes a recall benchmark at the full window in the pages consulted. The trade-offs between stuffing a long window and retrieving only what is needed are covered in RAG versus long context.

Effort settings change the picture as much as the model choice does. Every proprietary model in the top five bills hidden reasoning as output tokens, and on the Artificial Analysis index, moving Opus 5.5 from high to max effort raised cost per task from $1.82 to $5.98, while moving Sonnet 5.5 from xhigh to max raised it from $2.75 to $7.67. The mechanics are set out in the reasoning-model era has a cost problem.

Restricted and other notable models

Several important models did not qualify, either because access is restricted or because they fell just outside the top ten.

• Gemini 4 Argon (Google). It is first on LMArena’s text leaderboard at 1525 and scores 53 on the Artificial Analysis index at high effort. Google reports 77.9% on DeepSWE v1.1 and an output limit of 1M tokens. It is rolling out first to cyber defenders in the Fairwind Program, with paid API customers next at an undisclosed date. Introductory pricing is $2 / $10, rising to $4 / $20. It would likely enter this list on general release.
• Claude Mythos 5.1 (Anthropic). The same model as Fable 5.1 with more permissive safeguards, limited to vetted users in Anthropic’s Cyber and Life Sciences verification programmes.
• GLM-5.3 (Z.ai). Second among open-weight models on the Artificial Analysis index at 45, priced there at $1.40 / $4.40. It missed the list because it is text-only, generated 210M tokens on the index against a 140M median, and ships under a custom GLM-5.3 License.
• Grok 4.7 (xAI). xAI’s documentation lists it as the latest Grok model, with a 500K context at $2 / $6 below 200K prompt tokens. No independent score for it was found in the sources consulted, so it was not ranked.
• Qwen3.8-Max (Alibaba). Second on LMArena’s vision board at 1301 and 1482 on text. It is a proprietary model and its pricing and model card were not verified for this survey.

Routing across several models in production

The ranking above has a practical corollary: there is no single best model to standardise on. The top five change within a quarter, the price spread between Opus 5.5 and DeepSeek V4.1 Flash is more than sixteen times on output tokens, and each provider has its own outages, rate limits and release schedule. Production teams usually handle this with an AI gateway, a proxy between applications and model providers that exposes one API and decides where each request goes.

Three problems drive the decision.

Failover. When a provider returns errors or rate-limits a key, requests should move to a second model without an application change. Bifrost, Maxim AI’s open-source (Apache 2.0, Go) gateway, documents this as retries nested inside fallbacks: each provider gets its own retry budget with exponential backoff, then the request moves to the next provider and model in the chain. A primary and two fallbacks with three retries each allow up to twelve attempts before the original error returns. A reasonable chain from this list would be Opus 5.5, then GPT-6 Astra, then Gemini 3.8 Flash, so that an outage at one lab degrades quality rather than availability. The design questions are covered in more depth in the Frontier Wire piece on LLM failover and load balancing.

Cost control. Routing by task lets a cheap model handle classification while a flagship handles planning. The Bifrost LLM gateway puts more than 20 providers behind one API and enforces budgets and rate limits on virtual keys, the gateway-issued credentials that stand in for real provider keys. Its governance controls scope each virtual key to particular providers and models, so a team’s key can be barred from the $50-per-million output tier altogether. Maxim reports 11 µs of added overhead at 5,000 requests per second on a t3.xlarge instance against a mocked upstream, a vendor benchmark worth reproducing at your own payload size. Clustering, guardrails and SSO are on Bifrost’s paid enterprise tier.

Provider lock-in. Every model in the top five has its own API shape, effort parameter and caching rules. A gateway that speaks one format to applications lets a team move traffic to a new leader, such as Gemini 4 Argon when it opens, by changing configuration rather than code. Measuring whether the switch helped needs per-model data: gateway-level observability in Bifrost records tokens, cost and latency per provider, model and virtual key, exports Prometheus metrics and emits OpenTelemetry traces. For a broader comparison of gateway options, see the ranking of AI gateways.

Limits of this survey

This ranking reflects public information on 5 October 2026 and will be refreshed monthly. Several limits apply.

• No first-hand testing. No model was run, timed or evaluated for this survey. Speeds and cost per task are Artificial Analysis measurements; benchmark scores labelled as vendor-reported come from each lab’s own post or model card.
• Vendor benchmarks are not comparable across labs. The DeepSWE v1.1 figures in this survey range from 67.5 (Kimi K3) to 88.8 (Muse Spark 1.3), and Google cites 77.9% for Argon as a lead. Different harnesses and effort settings explain gaps of that size. Only the Artificial Analysis and LMArena numbers were measured under one method.
• Leaderboards move fast. Sonnet 5.5, GPT-6 Astra and MiMo-V2.6-Pro were too new to appear in LMArena’s top 25 on 2 October. Epoch AI’s full ECI table loads dynamically and only its top entry could be read.
• Not covered. Image, video, speech and embedding models; small on-device models; fine-tuning options; rate limits and regional availability; and data-retention terms beyond what is noted. Enterprise and committed-use discounts were not compared.
• What would change the ranking. General release of Gemini 4 Argon, an independent long-context recall benchmark at 1M tokens, or LMArena scores for the models released in late September.

Verdict

Claude Opus 5.5 is the best AI model available in October 2026 on the independent evidence: it leads the Artificial Analysis Intelligence Index and the Epoch Capabilities Index and costs less than half as much per token as GPT-6 Astra or Claude Fable 5.1. Astra is the stronger choice where long tool-using research and computer use matter more than latency. For most production budgets, the $2 / $10 tier of Claude Sonnet 5.5 and GPT-6.1 Sol delivers most of the frontier at a fifth of the price, and Gemini 3.8 Flash is the clear pick for video and audio input. Among open-weight models, MiMo-V2.6-Pro’s MIT licence and top index score make it the first to evaluate, with Kimi K3 and DeepSeek V4.1 Flash as the multimodal and low-cost alternatives. The most durable decision is not which model to choose but how easily the choice can be changed next month.