The Best LLMs in 2026: A Plain-English Comparison

The model at the top of the leaderboard may be too expensive for everyday work. The cheapest API may need more attempts to get the answer right.

That leaves a practical question: which models deserve a test on your work? A team reviewing contracts needs different evidence from one building a coding agent or processing thousands of images.

This guide compares the main model families and the costs behind their headline prices. The October 8 snapshot adds Anthropic’s Claude Haiku 5.5, released October 7, and the public preview of Mistral Large 4. It reflects Anthropic’s cheaper cache reads for Claude Sonnet 5.5 and also covers GPT-6.1 Sol, Gemini 4 Argon (still in limited access), GPT-6 Sol and Luna, Claude Opus 5.5, Grok 4.7, MiMo-V2.6-Pro, and MiMo-V2.6-Flash, and explains where the free MindsHub Air model and Jev fit. Scores use Artificial Analysis’s Intelligence Index v4.3.2; they should not be compared with scores from earlier index versions.

Where to start testing

• Difficult reasoning and coding: compare Claude Opus 5.5, Claude Sonnet 5.5, and GPT-6 Astra. They are among the leaders in this snapshot, but their task costs differ substantially. Gemini 4 Argon also scores 53, but access is limited to trusted cyber defenders for now, so treat it as a name to watch rather than a model to run today.
• Everyday work: test GPT-6.1 Sol for coding and multi-step workflows, and GPT-6 Luna or Claude Haiku 5.5 for focused, high-volume tasks. Compare them at the effort level you would use in production.
• Mixed-media input: Gemini 3.8 Flash accepts text, images, audio, and video. Muse Spark 1.3 and Qwen3.8-Flash-Next are also candidates when you need image or video input.
• Lower-cost text and image work: put MiMo-V2.6-Pro, MiMo-V2.6-Flash, DeepSeek V4.1 Flash, and GLM-5.3-Flash on the shortlist. All four have downloadable, MIT-licensed weights.
• More control over deployment: compare GLM-5.3 and Kimi K3 as well. Check their licences and hardware requirements before choosing to host them yourself. Mistral says Large 4’s weights will follow by the end of October, but it has not named their licence.

These are starting points. Run the same tasks through two or three candidates and inspect the results before committing.

Models at a glance

Prices are US dollars per million uncached input and output tokens. The benchmark setting appears beneath each model name. We use the highest published reasoning effort, including Grok’s xhigh row. When a source publishes one reasoning row without an effort label, we say so. Index scores are rounded to whole numbers, so models with the same score can still differ slightly. All figures were checked October 8, 2026.

Cost per task is the benchmark’s weighted average token cost. It is neither a spending limit nor the cost of a result your team has accepted. The suggestions in the third column are our assessment of what to test, based on the capabilities and measurements discussed below.

Pricing and access details

The token rates shown are those recorded by Artificial Analysis, checked October 8, 2026. They are developer API rates; consumer subscriptions and MindsHub’s own rate card are separate.

• OpenAI: the list prices in the table apply to prompts up to 272K input tokens. Above that, the whole request costs twice the input rate and 1.5 times the output rate (OpenAI pricing). GPT-5.6 Sol’s $4 / $20 is a promotional rate that OpenAI says is available at least through November 21, 2026. GPT-6.1 Sol also has an Ultrafast mode, added October 8, that streams faster at six times the standard rates. Check caching terms and tool fees as well.
• Anthropic: Claude Haiku 5.5’s $0.10 / $0.50 applies to prompts up to 100,000 tokens. A longer prompt pays $0.50 / $2.50 on the whole request, and cached tokens count toward that threshold (Anthropic pricing). On October 7, Anthropic halved Claude Sonnet 5.5’s cache-read price to $0.10 per million tokens without changing its input or output rates.
• Grok: long prompts can cost more. Check the provider’s current context thresholds, caching terms, and tool fees.
• Gemini: Google calls the 3.8 Flash rate introductory pricing through December 31, 2026, and lists $1.50 input / $7.50 output from January 1, 2027. For Gemini 4 Argon, Google has announced an introductory $2 / $10 that rises to $4 / $20, and has not said when the introductory period ends. Artificial Analysis’s $1.99 task cost uses the introductory rates and puts the same task at $3.98 at the standard rates.
• Muse: the table uses Standard pricing. Contributor has different data-use terms and is discussed below.
• Mistral: the table uses Large 4’s list rates. Mistral took 50% off for two weeks from the October 6 launch (Mistral’s changelog).
• DeepSeek: the V4.1 Flash row uses peak rates of $0.30 input / $1.20 output. Its off-peak rates are $0.15 / $0.60, and cached input has a separate rate. V4 Pro also has peak and off-peak pricing. Check the current rate card for the applicable hours and rates.
• Open weights: download access does not promise a particular licence, hosting price, or fit on your hardware. Review the exact checkpoint and host.

This article covers a wider set of models than MindsHub serves. Use the MindsHub model board to check availability and pricing for MindsHub billing.

What each model family offers

OpenAI: Astra, Sol, and Luna

GPT-6 Astra is OpenAI’s model for its hardest work across code, browsers, and professional files. At max effort it scores 53 in this snapshot, alongside Fable 5.1 and Gemini 4 Argon, at $3.26 per benchmark task. That makes it a useful candidate for complicated workflows with several tools and review steps.

GPT-6 Sol and Luna extend that family at lower token prices. Sol lists $2 input / $10 output per million tokens, half GPT-5.6 Sol’s promotional rates. Luna lists $0.10 / $0.50, down from $0.20 / $1.20. Both accept text and image input. Luna is worth testing for focused tasks at volume.

At max effort, Sol scores 48 at $1.04 per benchmark task, while Luna scores 38 at $0.07. GPT-5.6 Sol scores 47 at $1.99, and GPT-5.6 Luna scores 37 at $0.18. Those older rows remain as upgrade references. The newer models cost less on this benchmark; test whether that advantage holds for results your team accepts.

GPT-6.1 Sol, which OpenAI released in its API as gpt-6.1-sol on September 29, lists the same $2 input / $10 output rates as GPT-6 Sol for prompts up to 272K input tokens, with cached input at $0.10 instead of $0.20 per million. Artificial Analysis scores it 52 at $0.72 per task at max effort, against 48 at $1.04 for GPT-6 Sol, so it scores higher and costs about 31% less. Cost per task reflects cached-input pricing and the tokens each model uses, not only the list rates, so confirm the saving on your own workload. GPT-6.1 Sol also sits one point below Astra at under a quarter of Astra’s task cost.

Effort is part of the choice. Astra’s xhigh row scores 52 and costs $2.31 per task, against 53 and $3.26 at max, and GPT-6.1 Sol’s xhigh row scores 51 at $0.39, against 52 and $0.72. Compare the settings on your own evaluation set before paying for the extra reasoning.

Claude: strong results, and effort decides the cost

Claude Opus 5.5 leads this snapshot at 58 and $5.98 per benchmark task at max effort with the default fallback. Its high-effort row scores 54 at $1.82. Those measurements make effort a practical part of the choice, especially for recurring code and document work.

Claude Sonnet 5.5 scores 56 at $5.46 per task at max with fallback. Its $2 / $10 token rates are half Opus 5.5’s, yet its measured task cost is only about 9% lower. That figure reflects Anthropic’s October 7 cut to Sonnet 5.5’s cache-read price; at the old price, Artificial Analysis measured $7.62. Xhigh scores 52 at $2.01; high scores 47 at $0.88. Choose the setting as carefully as the model.

Claude Haiku 5.5, released October 7, is Anthropic’s small model for high-volume work and the first Haiku with an adjustable effort setting. It accepts text and image input with a one-million-token context window. At max effort, Artificial Analysis scores it 43 at $0.21 per task, against 17 for Haiku 4.5. It lists the same $0.10 / $0.50 rates as GPT-6 Luna and scores five points higher, but costs three times as much per task because it uses about three times as many tokens: it writes about three times as many output tokens and re-reads about three times as much cached context. Its xhigh row scores 41 at $0.12, and high scores 38 at $0.08. At max, its median wait for the first answer token is about five minutes, so test lower settings for anything a person waits on.

Fable 5.1 remains at 53 and $7.63, while Opus 5 scores 51 at $5.86. Sonnet 5 has lower token rates, but its max-effort task cost is still $5.09. We retain the older rows for existing users comparing an upgrade. Opus 5.5, Sonnet 5.5, and Fable measurements include a default fallback; Artificial Analysis explains the Fable setup in an article that uses an earlier index version, so its scores differ from the table. Treat each result as a measurement of the named configuration.

Gemini: Flash for mixed input, Argon in limited access

Gemini 3.8 Flash accepts text, images, audio, and video. That range is useful when a workflow combines recordings, screenshots, and documents. Its high-effort row scores 41 at $1.24 per benchmark task.

Gemini 3.8 Flash’s broad score is below the leaders, so check the tasks that matter to you. Google also notes that difficult work can use more tokens and tool calls than with 3.7 Flash. Include those calls in the evaluation, along with the introductory pricing deadline.

Gemini 4 Argon is not broadly available yet. Google announced it on September 30 and is rolling it out to trusted cyber defenders through its Fairwind Program, with paid API customers and Google AI Ultra subscribers next and no date given.

Artificial Analysis lists a single Argon row, high, which it calls the highest effort available. It scores 53 on index v4.3.2, the same rounded score as Astra and Fable 5.1 (52.56 against 52.67 and 53.35 before rounding) and below Opus 5.5 and Sonnet 5.5, at $1.99 per task on Google’s introductory rates, or $3.98 at the standard $4 / $20. Argon averages about 62k output tokens per task against about 27k for Astra at max, so its low task cost comes from the discounted token price, not shorter answers. Google describes image and long-video understanding but has not published a list of input types, and Artificial Analysis has no speed or latency figure for it yet.

Google’s own launch table, which uses benchmarks it chose and many results it computed itself, shows Argon leading outright on 13 of its 19 rows and tying one, against Astra, Fable 5.1, and Opus 5.5. Artificial Analysis’s independent index does not show Argon ahead of Opus 5.5, Sonnet 5.5, or Fable 5.1. Google also says it is still strengthening safeguards before a broad release.

Muse: long agent workflows, with a separate Contributor option

Muse Spark 1.3 supports text, image, and video input. At max effort, its score of 48 and Standard task cost of $1.60 make it worth comparing for long coding and agent workflows. It is a closed model; Meta’s history with Llama does not mean Spark weights are downloadable.

Meta’s Contributor option charges $0.10 per million input tokens and $0.20 per million output tokens in exchange for permission to use submitted prompts and completions in training. Treat the data terms as part of the price. The benchmark figure here uses Standard and does not establish a Contributor task cost.

Grok: search access and an effort setting to test

Grok 4.7 adds a newer candidate for long coding and knowledge-work tasks. It accepts text and images, with a 500k context window and access to the provider’s web and X search tools. Search can supply current information; the model still needs to check and use it correctly.

At xhigh, Grok 4.7 scores 46 and costs $3.74 per benchmark task. High also rounds to 46 at $2.73. Grok 4.6 remains at 44 and now costs $1.75 at xhigh. Both generations list $2 input / $6 output per million tokens, yet the newer model costs more than twice as much per task. The table uses the standard 4.7 API, not its separately priced fast variant.

GLM and Kimi: downloadable models with different costs

GLM-5.3 is a text model with a score of 45 and a task cost of $2.01. GLM-5.3-Flash adds image input, uses an MIT licence, and reaches 42 at $0.25 per task. The larger model’s custom licence and higher cost deserve separate consideration.

Kimi K3 supports image input and long coding workflows. Its max-effort score is 44 at $2.00 per task. Downloadable weights give you a hosting option, but its custom licence and hardware needs still determine whether that option works for your team.

MiMo: two low-cost multimodal candidates

MiMo-V2.6-Pro scores 46 at $0.13 per benchmark task, using the API rates recorded by Artificial Analysis: $0.435 input / $0.87 output per million tokens. It is worth testing when cost matters, though one broad score cannot show whether it handles your tasks reliably.

Xiaomi’s Pro-RL model card lists text, image, audio, and video input, a one-million-token context window, and MIT-licensed weights. With 1.02 trillion total parameters, it needs substantial hardware to self-host.

MiMo-V2.6-Flash scores 38 at $0.06 per task on $0.14 input / $0.28 output rates, the lowest task cost in the table. Artificial Analysis calls it slower than average and very verbose, so the low cost comes from the low token rates rather than short answers. Xiaomi’s Flash-RL model card lists text, image, audio, and video input, a one-million-token context window, and MIT-licensed weights, with 309 billion total parameters and 15 billion active.

Qwen: distinguish Max from Flash-Next

Qwen3.8-Max (0902) is a proprietary multimodal API. Qwen3.8-Flash-Next has open weights, text, image, and video input, and a 256k context window.

The current 0902 Max release scores 45 at $5.41 per task; Flash-Next scores 40 at $0.37. The older 0803 Max release has different measurements, so record the release as well as the model name. Context windows, supported input types, and task behavior also differ. The downloadable Qwen3.8 2.4T A95B checkpoint is another distinct model; it is not the Max API.

DeepSeek: V4.1 Flash replaces the older Flash API

DeepSeek V4.1 Flash launched on September 10 with native text and image input, a one-million-token context window, and MIT-licensed weights. Artificial Analysis measures it at 39 and $0.27 per task at max effort. That puts it close in task cost to GLM-5.3-Flash, with a different capability profile to test.

DeepSeek’s current API documentation names the new model deepseek-flash. The older V4 Flash and Flash Vision API names now route to V4.1 Flash. V4 Pro remains available: the current documentation withdraws the earlier plan to replace it after September 14 and says its billing will stay unchanged. Check the model behind an alias when comparing older results.

The name Flash does not mean it will run on a laptop. Its model card lists 552 billion backbone parameters plus a 196-billion-parameter Engram memory component; self-hosting needs a suitable serving setup and hardware budget.

Mistral: Large 4 in public preview

Mistral Large 4 entered public preview on October 6. Mistral calls it its largest model: a mixture-of-experts with about 1 trillion total parameters and 52 billion active per token, and it reads images as well as text. Mistral’s model card lists a one-million-token context window, while Artificial Analysis and OpenRouter record 524,288 tokens for the preview API.

Artificial Analysis scores the preview 38 at $1.13 per task on the $1.36 / $4.18 list rates. Mistral says the reinforcement-learning run behind the preview is still going, so later results can differ. It plans to release the weights by the end of October and has not yet named their licence. Until then, treat Large 4 as a hosted API to evaluate, not as a model you can run yourself.

MindsHub free tier

The MindsHub free tier includes selected models at no charge. Currently included: the MindsHub Air model and Jev. No credit card is required; fair use and daily rate limits apply.

Make the final choice on your own work

Choose representative tasks and agree on what a usable result looks like. Check correctness, required input types, tool use, response time, and data-handling terms. Then divide the total spend, including failed attempts, by the number of results you would accept.

Our cost-per-task guide explains that calculation. The model selection guide covers the evaluation and hosting decisions. If you are changing the workspace as well as the model, use the workspace migration checklist to preserve instructions, connections, and reusable work.

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