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 September 11, 2026 snapshot includes DeepSeek V4.1 Flash, Qwen3.8-Flash-Next, and GLM-5.3-Flash alongside the larger models. Scores use Artificial Analysis’s Intelligence Index v4.3; they should not be compared with scores from earlier index versions.

Where to start testing

• Difficult reasoning and coding: compare GPT-6 Astra, Claude Fable 5.1, and Claude Opus 5. They lead this snapshot, but their task costs differ substantially.
• Everyday work: test the GPT-5.6 family at the effort level you would use in production. Start with Terra, then check whether Sol improves the results enough to justify the extra spend.
• 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 DeepSeek V4.1 Flash and GLM-5.3-Flash on the shortlist. Both 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.

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 with complete measurements, including Grok’s xhigh row. When a source publishes one reasoning row without an effort label, we say so.

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 September 11, 2026. They are developer API rates; consumer subscriptions and MindsHub’s own rate card are separate.

• OpenAI and 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.
• Muse: the table uses Standard pricing. Contributor has different data-use terms and is discussed below.
• 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 and the GPT-5.6 family

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, at $3.26 per benchmark task. That makes it a useful candidate for complicated workflows with several tools and review steps.

The GPT-5.6 family gives you three other price points: Sol for complex professional work, Terra for a balance of cost and capability, and Luna for high-volume tasks. Test these on routine drafting, extraction, and analysis before paying Astra’s rate for every call.

Effort is part of the choice. Astra’s xhigh row also rounds to 53 and costs $2.31 per task. The matching rounded score does not prove the settings are equivalent, but it gives you a reason to compare them on your evaluation set.

Claude: strong results with substantial reasoning costs

Claude Fable 5.1 and Opus 5 are candidates for difficult reasoning, coding, and review. Fable 5.1 scores 53 and Opus 5 scores 51 here. Sonnet 5 offers lower token rates for teams already using Claude, though max effort can still produce a large bill.

The four Claude entries have the highest task costs in this table. Fable 5 remains as a reference for existing users; new evaluations should include Fable 5.1. Fable’s measurements also include a fallback model, as Artificial Analysis explains. Those results describe that tested configuration, not every request to Fable.

Gemini: one model for several input types

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.

The 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.

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 4.6 is a candidate when the workflow needs the provider’s web and X search tools. Those tools supply current information; the model still needs to check and use it correctly.

At xhigh, Grok scores 44 and costs $2.32 per benchmark task. Its high row also rounds to 44 at $1.86. The lower token rate alone would not reveal that difference. Compare both settings before choosing one for recurring work.

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.

Qwen: distinguish Max from Flash-Next

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

Both round to 40 on this index, yet Flash-Next costs $0.37 per task against Max’s $2.67. Their context windows and behavior differ, so the shared score is a reason to test the cheaper option, not evidence that the two are interchangeable. 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 40 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.

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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