Best AI Model Right Now (2026): The Frontier Models, Ranked by Score and Price

The best AI model depends on the job: Claude Sonnet 5.5 or GPT-6.1 Sol for everyday coding, GPT-6.1 Terra for bounded screen-based agents, Claude Haiku 5.5 or GPT-6 Luna for cheap volume, and Kimi K3.1 Pro for document-heavy research. Claude Opus 5.5 remains the premium candidate for difficult, open-ended work. Standardise on a default and an escalation path, with the prices and limits below deciding where each earns its place.

The best AI model right now, in one table

Choose by job before comparing the overall scores. For a founder or team lead choosing this month's default:

• Coding: start with Claude Sonnet 5.5, Anthropic's everyday coding model, or GPT-6.1 Sol, OpenAI's balanced model for complex work. Escalate difficult repository changes to Claude Opus 5.5. Kimi K3.1 Pro is a serious challenger for long coding runs; Terra belongs on scoped fixes before whole-project work.
• Agents: an agent is a model connected to tools that can take actions. Put GPT-6.1 Terra first for defined screen workflows, such as updating a record and checking that it saved. For open-ended work across many steps, start with Opus 5.5 or Sol; compare Kimi on long coding and research workflows.
• Cheap high-volume work: start with Haiku 5.5 in Claude or GPT-6 Luna in OpenAI for labels, extraction and short summaries. Terra and Kimi are escalation candidates, not the cheapest defaults.
• Long documents: put Kimi K3.1 Pro, Moonshot's new reasoning model, first on the evaluation list for document-heavy research and cross-document synthesis. Choose Gemini 3.8 Flash, Google's lower-cost multimodal model, when the same workflow also needs audio or video. A large context window means more material fits, not that every detail will be retrieved correctly.

Since October 14, 2026, Kimi K3.1 Pro arrived on October 23 and GPT-6.1 Terra on October 26, adding a long-workflow challenger and a cheaper visual-agent option to the shortlist.

Verified October 28, 2026: model capabilities, access and prices were checked against the makers' pages. These are editorial buying recommendations based on that evidence, not a hands-on comparison. The scores below come directly from the Artificial Analysis model leaderboard, now using Intelligence Index v5.0; they must not be compared numerically with the previous verification's index version. Prices are USD per million uncached input / output tokens at Standard service unless marked otherwise. A token is a unit of text the provider processes and bills.

The scored rows are ordered by the displayed AA score, not a universal purchase ranking. “Max” is a reasoning setting, not a subscription plan; “Not shown” means the current leaderboard supplies no index score for that entry. Token rates exclude paid tools, cache writes, hosting and retries, and models can consume different numbers of tokens to finish the same job.

OpenAI's long-context rule matters: for Astra, Sol, Terra and Luna, prompts above 272,000 input tokens multiply input and cache rates by 2 and output rates by 1.5 for the full request. Kimi K3.1 Pro has no context-length price tier. Keep these billing thresholds separate from the maximum amount of material a model can hold. OpenAI model specifications, Kimi API guide.

How "best" is actually measured

Use each benchmark for the question it answers. Artificial Analysis combines several kinds of work into an overall index. A coding-specific result answers a narrower question; do not treat the two as interchangeable.

The current Artificial Analysis methodology is Intelligence Index v5.0, combining 9 evaluations. It is primarily a text-based, English-language suite; image, speech and multilingual evaluations are separate. A lead here does not establish a lead on every kind of input. Historical scores from a different version of the index are not a clean measure of improvement or decline.

Settings matter too. On this verification, Sonnet 5.5 scored 54 at max with fallback, 41 at high with fallback, and 30 at medium with fallback. Anthropic documents Medium as the default in Claude apps and Claude Code, and High on the Claude Platform. Do not assume the top table describes the response speed or result of an untouched default configuration. Sources: AA leaderboard, Anthropic's Sonnet release.

No SWE-bench scores are used in this ranking. The table stays on the Artificial Analysis index. Moonshot's coding result below is attributed separately, not mixed into that ranking. Use the coding-specific model guide for the workflow comparison, and compare the exact model, agent setup and benchmark version before making a coding purchase.

Claude Opus 5.5 and Fable 5.1: the ceiling must earn its price

Claude Opus 5.5 is the first premium Claude model to evaluate for difficult work. It leads the AA table above. Anthropic recommends Claude Fable 5.1 for demanding reasoning and long-running agents when Opus evaluations still fall short. That makes Fable a specialist alternative to test against your hardest failures, not the automatic winner because it has the more expensive name. Claude model guide.

For a CTO commissioning a codebase migration or a founder reconciling conflicting research, the extra capability may pay for itself through fewer corrections. Anthropic positions Opus 5.5 for complex coding and enterprise work. That is a reason to include it in the evaluation, not a guarantee that it will solve your repository or make a high-stakes decision safely. Opus 5.5 release.

The practical comparison is Opus against Sonnet on the same tasks. At equal token counts, Opus costs twice as much. Keep it where it measurably reduces failures or review time; route work that already passes to the cheaper model. Fable costs five times Sonnet's token rates, so it needs a stronger justification still.

Claude Sonnet 5.5 and Haiku 5.5: the everyday workhorses

Claude Sonnet 5.5 is the sensible starting point for everyday work inside Claude; Haiku 5.5 is the candidate for repetitive work. Evaluate Sonnet across drafting, document work and bug fixing. Haiku makes narrowly scoped summaries, classification and subagent tasks much cheaper to attempt. Those are Anthropic's stated intended uses, not a promise that both models produce equivalent work. Sonnet release, Haiku release.

Haiku's cheapest rate applies only to prompts of 100,000 tokens or fewer. Above that, use the higher band in the table. Long instructions, retrieved documents and conversation history all add to the input. Do not budget an entire long-document workflow at the headline rate. Current Claude pricing.

For a support operator, start by checking whether Haiku can assign the correct category and extract the requested fields. Send ambiguous cases to Sonnet, then review the cases that remain uncertain. That routing rule is more useful than buying Opus for every message. The Haiku 5.5 pricing and switching guide covers the model in depth; the chatbot comparison covers the surrounding apps.

GPT-6.1 Sol, Terra, Astra and Luna: choose the API model separately from ChatGPT

GPT-6.1 Sol remains the balanced OpenAI starting point for complex work; Terra is the first alternative to evaluate when the task is bounded and visual. OpenAI positions Astra for the most demanding work and Luna for focused, high-volume tasks. Sol competes directly with Sonnet on base token price; the new GPT-6.1 Terra guide covers where the cheaper option fits. OpenAI models.

Terra accepts text and images, returns text, and has a 1,050,000-token context window. OpenAI recommends it for interpreting technical drawings, working from application screens and drafting or revising supplied material. Its model-selection guide also names scoped code fixes. For an operator updating product records from screenshots, that is a more relevant fit than choosing the largest reasoning model by habit. For a CTO commissioning a broad codebase migration, keep Sol and Astra in the comparison. Terra specifications, OpenAI selection guidance.

Terra tool calling requires the Responses API. Its default reasoning effort is medium; the max-effort leaderboard row is a different operating point. Before moving an agent, check both the saved result and the completion report. A model that says a record changed has not proved that it did. These checks matter more than winning a small score difference.

For an application already built around OpenAI's tools, compare Astra on tasks Sol fails, Terra on scoped visual work, and Luna on tasks that pass without extensive reasoning. Functions, web search, file search and computer use appear in the model catalog, but the application must configure those tools. A model choice alone does not connect your business systems.

Keep the API model ID separate from the ChatGPT experience and its plan access. Our GPT models guide maps the names and where they run; the GPT-6 model and access guide covers the broader family. An API price is not a ChatGPT subscription quote.

Kimi K3.1 Pro: a long-workflow challenger, not a cheap routing model

Kimi K3.1 Pro belongs near the top of a document-heavy research shortlist and in the coding-agent comparison. It has a 1,048,576-token context window and flat token rates across context lengths. That makes it worth evaluating for a founder reconciling a large data room or a team lead asking an agent to trace a change across a large repository. The Kimi K3.1 Pro guide covers the switch in depth. Kimi pricing and context, API guide.

The evidence is stronger than a large context number alone: Moonshot reports 65.7% on Terminal-Bench 4.0, a test of terminal-based coding tasks, and describes improved cross-document financial research. That is a maker-reported result, not a test performed for this article or proof that Kimi beats every alternative on your repository. Its overall AA position remains below Opus, Sol and Terra in this snapshot. Use it as a challenger for long tasks, not an automatic replacement for a working coding setup. Moonshot's release.

The cost limitation is always-on thinking. On the Kimi API, reasoning effort defaults to max and can be set to low or high; thinking cannot be disabled. For short labels or simple extraction, start with Haiku or Luna instead. For a large document set, require citations to the underlying passages and verify conflicting dates and figures before accepting the synthesis. Context capacity alone does not establish reading accuracy.

K3.1 Pro is available through Kimi's hosted products and API. Moonshot says full weights will arrive by November 4, 2026; that is a promised release, not evidence that you can self-host it today. In Kimi Code, Plus/Moderato and above have model access, while Pro/Allegretto and above unlock the full context allowance. A coding subscription's allowance is separate from API billing. Kimi Code model configuration.

Gemini 4 Argon and 3.8 Flash: access comes before the score

Gemini 3.8 Flash remains the lower-cost Google starting point; Argon is now available for harder work. Google has opened Argon beyond its initial trusted testers and cyber defenders. For a team already using Google's stack, that makes it a candidate to compare on complex coding and knowledge work. The general-availability announcement explains access; our Gemini 4 Argon guide covers access and switching.

Argon's introductory Standard rate matches Sonnet 5.5 and GPT-6.1 Sol, with the scheduled increase shown in the table. No free API tier is listed. Compare task results now while budgeting for the rate your production workload will eventually pay. Google pricing.

Gemini 3.8 Flash accepts text, images, audio, video and PDFs and produces text, with an input limit of 1,048,576 tokens. That makes it a useful candidate for a business combining meeting recordings, screenshots and written notes. For long documents with mixed media, put it ahead of Terra on the shortlist because Terra's inputs are text and images. Kimi remains the challenger for document-heavy reasoning rather than the cheapest multimodal option. A free API tier is listed, but Google marks free-tier data as used to improve its products; the paid tier is marked otherwise. Check that distinction before using customer material. Model documentation, Google pricing.

Gemini 3.1 Pro Preview remains listed and can stay in an existing workflow that has demonstrated a reason to use it. It has no free API tier listed, and its prompt-length price band matters for long documents. Its older score is not carried into the current index: the leaderboard shows no v5.0 score for that entry. Pricing.

Muse Wonder: keep the API model separate from Wonder Full

Muse Wonder is Meta's candidate for complex coding and long-running agents, available on Meta Model API and in Muse Code. The API model, muse-wonder-1.0, uses Standard mode; Contributor mode is not available for it on Model API. Meta pricing, model access.

Muse Wonder scores 43 at max on the AA leaderboard. Muse Wonder Full scores 50 at max, but Meta offers that separate variant only through Muse Code's Power Usage and Maximum Usage subscriptions, with no Model API access. Setting Wonder's API reasoning effort to max does not select Full. Meta model access.

For a builder considering Meta for a multi-file code change or a long-running tool workflow, compare the API model against Opus 5.5 at the same base token rates. Keep it only if its results or workflow fit justify the cost. Full's higher score is a reason to evaluate that subscription separately, not evidence that the API model delivers it.

Grok 4.7: evaluate the workflow, not a cheapest-model slogan

Grok 4.7 is xAI's current flagship, but it is no longer the cheapest model in this comparison. Its higher price band starts at 200,000 prompt tokens and applies to the full request. The model has a 500,000-token context window. xAI pricing, model catalog.

For an operator with an existing xAI integration, it belongs in a comparison on extraction, drafting or tool-based work. Keep the scope concrete: whether it follows your schema, uses the right source and meets your response-time requirement. xAI explicitly says current events require enabled search tools; the model does not automatically know live information just because it is Grok. That distinction matters for research and monitoring applications. xAI model documentation.

Mistral Large 4: a preview to pilot, not weights to deploy today

Mistral Large 4 belongs in a pilot when its deployment direction fits your business. The October 6 release is a public API preview. Mistral says the weights will arrive by the end of the month; that promise is not a downloadable release you can plan today's self-hosted deployment around. Mistral announcement.

The current Mistral API pricing page no longer shows the earlier sale: use the Standard rate in the table. The model documentation still labels Large 4 as a public preview. Its AA score is included with that preview label intact.

For an enterprise builder considering a Mistral-based document workflow, evaluate the preview against the same acceptance criteria as your incumbent. Keep a working fallback until the version, commercial terms and deployment artifacts you need are available. The Mistral Large 4 guide covers the preview and planned weights in detail.

GLM-5.3, DeepSeek and the open-weight frontier

Open weights buy deployment control, not free inference. GLM-5.3 from Z.ai and DeepSeek V4.1 Flash have maker-hosted model repositories; GLM's card includes local-serving guidance, and DeepSeek publishes its weights under the MIT license. Infrastructure, capacity planning, maintenance and security still cost money. GLM-5.3 model card, DeepSeek V4.1 Flash model card.

Z.ai offers hosted GLM-5.3 and the cheaper GLM-5.3-Flash. Neither is the same purchase as running the weights on your own infrastructure. A company that needs local processing should compare total serving cost at its expected utilization, not treat the absence of a vendor token bill as a zero-cost system. Z.ai pricing.

DeepSeek's current Flash API model is DeepSeek V4.1 Flash 1017, accessed through deepseek-flash. Its peak and off-peak rates are shown in the table. The older V4 Flash and V4 Flash Vision experimental models are retired and their aliases now serve the 1017 revision. Do not evaluate an old alias as though it still identifies the original model. DeepSeek pricing and model details.

For a CTO with data-residency requirements, deployment control can outweigh a leaderboard gap. For a solo builder without infrastructure support, a hosted API is often the more practical first evaluation. The open-source LLM guide covers the wider field and licensing questions.

Which model should you actually pick

The right model is set by your situation, not by the top of the leaderboard. Match the job first, then compare the candidates on your own examples.

The one rule that decides it

The deciding question remains: how much does a wrong answer cost you? Include the price of review, rework and delay. Cheap models earn routine work when they pass your checks. Premium models earn difficult work when they improve the result enough to justify the full cost. A leaderboard is a starting point for that decision, not permission to skip it.

Next week, take a representative sample of one recurring job, define what a correct result looks like, and compare your current model with a cheaper candidate and a stronger candidate. Record accepted outputs, billable usage, retries and review time. Move the work only when the replacement improves that combination.

What is the best AI model right now?

For everyday coding and business work, start with Claude Sonnet 5.5 or GPT-6.1 Sol. Put Terra first for bounded screen workflows, Haiku or Luna first for cheap volume, and Kimi K3.1 Pro on the document-heavy research shortlist. Opus 5.5 leads the current AA index, but the job-specific choice depends on results, access and total cost.

What is the best free AI model?

Gemini 3.8 Flash is a practical free-API candidate where its quotas and data terms fit. GLM-5.3 and DeepSeek V4.1 Flash are candidates for local deployment, but downloading weights does not eliminate hardware or operating costs. A free chatbot plan and free API usage are also different products.

What is the best AI model for coding?

Start with Sonnet 5.5 or GPT-6.1 Sol, and compare Opus 5.5 or Astra on harder repository tasks. Kimi K3.1 Pro belongs in the long-task comparison; Terra belongs on scoped fixes. The agent environment and your repository's tests matter alongside the model. No SWE-bench score is used here.

What is the best AI model for research and writing?

Compare Claude, GPT-6.1 Sol and Kimi K3.1 Pro for document-heavy synthesis; choose Gemini 3.8 Flash when audio and video inputs matter, and evaluate Terra for concise writing from supplied material. For live research, verify the tools retrieving the sources as well as the model writing the answer.

Is the most expensive model always the best?

No. Fable 5.1 and GPT-6 Astra both have higher base token prices than Opus 5.5, which leads this AA snapshot. A cheaper model can be the better purchase if its outputs pass your checks with less total spending on tokens, retries and review.

Want the full map of which AI tool fits which job, without the hype? Get the AI tools map for business owners, free.

Get the AI tools map for business owners

A plain-English guide to which AI models and tools actually fit which job, updated as the frontier moves. Free to subscribers.