The best AI reasoning models in 2026
The best AI reasoning models right now are Gemini 4 Argon, Claude Fable 5.1 and DeepSeek-V4-Pro, out of 10 we have reviewed. Each is ranked by our rating, with its price, our verdict and who it suits.
Updated Oct 23, 2026 · Ranked from our reviews · How we review
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Google is back at the frontier with Argon, and its native video and audio input is something the Claude and GPT flagships still cannot match.
Best for: Teams working with video, audio or very large document collections who want a top-tier reasoning model.
Price: API: $2 input / $10 output per 1M tokens until 30 Nov 2026, then $4 / $20; Batch and Flex half price; cached input $0.10 (then $0.20). No free tier.
Read our Gemini 4 Argon review4.5/5 - 2
Three weeks after launch, Anthropic's own cheaper Opus 5.5 overtook Fable 5.1 on most published benchmarks, which leaves it a specialist's model at a generalist's flagship price.
Best for: Teams with long scientific or research agent tasks where Opus 5.5 at high effort has been tested and found short.
Price: API: $10 per 1M input tokens, $50 per 1M output tokens; cache writes $12.50 (5 min) or $20 (1 hour); cache reads $0.25; Batch API 50% off.
Read our Claude Fable 5.1 review4.3/5 - 3
DeepSeek-V4-Pro is frontier-class reasoning under an MIT licence at about a quarter of Opus 5.5's per-token price, provided your data can go to China or you own a rack of H200s.
Best for: Teams that want frontier-class open weights for coding and reasoning, either on their own eight-GPU server or through a very cheap API for non-sensitive work.
Price: Free MIT-licensed weights. DeepSeek API: $0.66 input / $1.98 output per 1M tokens off-peak, $1.32 / $3.96 at peak; cache hits from $0.022.
Read our DeepSeek-V4-Pro review4.3/5 - 4
Kimi K3 tops Moonshot's own agent scorecard, but at $2 per Artificial Analysis task it costs seven times GLM-5.3-Flash for two more index points.
Best for: Teams building browsing and research agents who want open weights and can self-host or pay frontier-like API prices.
Price: Open weights under a modified MIT licence. Kimi API: $3 input / $15 output per 1M tokens; cached input $0.30.
Read our Kimi K3 review4.3/5 - 5
A record-setting maths and science model that costs four and a half times as much per task as a sibling scoring one point less.
Best for: Research, maths and science teams that need the strongest reasoning OpenAI offers and can justify the price.
Price: API: $10 per 1M input tokens, $50 output; cached input $1; Batch/Flex half price; prompts over 272K input tokens cost 2x input and 1.5x output.
Read our GPT-6 Astra review4.2/5 - 6
An open flagship that follows instructions and reproduces research better than Claude or GPT on Qwen's numbers, though few will run the weights themselves.
Best for: Teams doing research, specification-heavy writing and multilingual work through an API who want an open-weights fallback.
Price: Open weights under the Qwen3.8-Max licence. Qwen Cloud API: $2 input / $6 output per 1M tokens; cache reads $0.17 (explicit) or $0.25 (implicit).
Read our Qwen3.8-Max review4.2/5 - 7
Fourteen months on, gpt-oss-120b is no longer the strongest open model, but it is still the plainest licence and the widest support for reasoning on one 80GB GPU.
Best for: Teams that need a permissively licensed reasoning model they can run privately on one GPU, with mature tooling.
Price: Free weights under Apache 2.0; hosted prices vary by provider.
Read our gpt-oss-120b review4.0/5 - 8
Granite 4.2 is the open family a risk committee will approve fastest, with reasoning that is good for its size and nowhere near the frontier.
Best for: Companies and developers who want small, permissively licensed reasoning models for local or on-premises agents.
Price: Free weights under Apache 2.0; watsonx and third-party hosting billed separately.
Read our Granite 4.2 review4.0/5 - 9
Falcon-H1R-7B posts maths scores far above its size, but almost nobody has adopted it and the independent tests are still to come.
Best for: Teams that need strong competition-style maths reasoning from a model small enough for a laptop, and whose lawyers accept a custom licence.
Price: Free weights under the Falcon LLM licence (royalty-free; attribution and acceptable use policy required). No public hosted API listed by Artificial Analysis.
Read our Falcon-H1R-7B review3.6/5 - 10
Phi-4-Reasoning-Vision-15B is an MIT-licensed vision model that loses to an 8B Qwen on Microsoft's own scorecard.
Best for: Teams with a data-centre GPU who need an MIT-licensed English vision model for charts, diagrams and screenshots and are prepared to compare it with Qwen3-VL first.
Price: Free weights under MIT; Microsoft Foundry hosting billed separately.
Read our Phi-4-Reasoning-Vision-15B review3.4/5