The Best LLMs in 2026: A Comparison and What to Expect in 2027

Amna ManzoorLast Updated on November 6, 2026

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Key Takeaways

Among 13 leading LLMs as of November 2026, context windows cluster near 1M tokens, so price tiers, long-prompt surcharges and license terms decide the shortlist.

• GPT-6 Astra and Claude Fable 5.1 cost $10 input and $50 output per million tokens. Anthropic suggests Opus 5.5 first.
• Long prompts cost more on OpenAI and xAI.
• Gemini 4 Argon rises from $2 input and $10 output to $4 and $20 on 1 December 2026, so Google pilots need the new rate.
• DeepSeek V4 Pro (MIT) and Qwen3.6-27B (Apache-2.0) use standard licenses. Mistral Large 4 and Muse Spark Open add commercial conditions.
• OpenAI no longer offers fine-tuning to new users.
• Gemini 4 Carbon is limited to some cyber defenders, with no date for wider access.

Pilot one closed and one open-weight model on the same real tasks, priced at the scheduled new rates.

GPT-6 Astra and Claude Fable 5.1 are the top-priced flagships from OpenAI and Anthropic, at $10 per million input tokens and $50 per million output tokens. Most rivals now list a context window of about 1M tokens, so window size rarely settles a shortlist. Price tiers, long-prompt surcharges and license terms do.

This guide compares the best LLMs in 2026, 13 leading closed and open-weight models, as they stand today. Every price and limit comes from vendor pages checked on 4 November 2026. Two scheduled increases matter right away. Gemini 4 Argon doubles in price on 1 December 2026, and Gemini 3.8 Flash follows on 1 January 2027.

Which are the best LLMs in 2026, and how do they compare?

Nine closed models and four open-weight models make the current shortlist. OpenAI, Anthropic, Google, xAI and Meta each sell tiered closed line-ups, while Mistral Large 4, DeepSeek V4 Pro, Qwen3.6-27B and Muse Spark Open publish their weights. Prices are per million input and output tokens at standard rates.

The "Best for" column follows each vendor's own positioning, with our reading where the vendor is vague. Treat it as a starting point. Gemini 4 Carbon, which Google announced on 28 October 2026, is left out of the table because only a small group of cyber defenders can use it today; it appears in the 2027 section below.

Which closed LLMs are worth testing now?

Seven closed models get a full profile here. The two budget tiers, Claude Haiku 5.5 and GPT-6 Luna, are covered in the table and the FAQ because their main question is price.

Claude Fable 5.1

Specifications: Anthropic lists Claude Fable 5.1 at $10 input and $50 output per million tokens, with a 1M-token context window and 128K maximum output. Anthropic commits to keeping it available until at least 1 September 2027.

Best fit: Demanding reasoning and long-horizon agentic work, which is how Anthropic positions it.

Caveat: Anthropic's models overview tells teams to start with Claude Opus 5.5 for most workloads and move to Fable 5.1 only when evaluations on Opus 5.5 at higher effort still fall short. That advice comes from the vendor selling both. For a head-to-head with OpenAI's flagship, see our GPT-6 Astra vs Claude Fable 5.1 comparison.

Claude Opus 5.5

Specifications: Claude Opus 5.5 costs $4 input and $20 output per million tokens, with a 1M-token window and 128K maximum output. Anthropic describes it as built for long-running agentic coding and knowledge work. Sonnet 5.5 sits one step below at $2 and $10.

Best fit: Most production workloads on Claude. It is Anthropic's default recommendation.

Caveat: Fast mode, which Anthropic says runs up to 2.5x faster, bills at twice the standard price. Claude Opus 5 also remains available as a legacy model, so confirm which model ID your code actually calls.

GPT-6 Astra

Specifications: OpenAI calls GPT-6 Astra "our most capable model for the most demanding work." It costs $10 input and $50 output for prompts up to 272K input tokens, with a 1.05M-token window and 128K maximum output.

Best fit: Complex reasoning and coding.

Caveat: Long prompts cost a lot more. Above 272K input tokens, OpenAI's API pricing page lists Astra at $20 input and $75 output, which doubles the input rate for anyone feeding it whole contracts, codebases or document sets.

GPT-6.1 Sol

Specifications: GPT-6.1 Sol costs $2 input and $10 output up to 272K input tokens, and $4 and $15 above that. It shares Astra's 1.05M window and 128K output limit.

Best fit: Balanced workloads. OpenAI describes Sol as "Near-Astra performance for complex work at a lower cost." GPT-6.1 Terra, at $1 and $5, is the next tier down for computer use and writing.

Caveat: Astra costs five times more per token. Run one evaluation set on both before paying for that gap.

Gemini 4 Argon

Specifications: Google describes Gemini 4 Argon as its flagship reasoning model for complex software engineering and deep mathematical and scientific problem solving. Input costs $2.00 through 30 November 2026 and $4.00 from 1 December 2026, and output moves from $10.00 to $20.00 on the same date. Google's launch post lists a 1M unified input-output token limit.

Best fit: Software engineering and reasoning over large codebases or document sets, in our view.

Caveat: The 1M limit covers input and output together. Budget at $4 and $20, the rate Google lists from 1 December 2026.

Grok 4.7

Specifications: xAI lists Grok 4.7 with a 500K context window, $2 input and $6 output below 200K prompt tokens, and $4 and $12 at 200K or more.

Best fit: Short and medium prompts that produce long answers. Here the $6 output rate undercuts GPT-6.1 Sol's $10.

Caveat: Once a prompt reaches 200K tokens, xAI bills the higher rate for every token in that request. Grok 4.3 is cheaper at $1.25 and $2.50 and lists a 1M window, which may suit long-document work better.

Muse Spark 1.3

Specifications: Meta calls Muse Spark 1.3 "the latest version, tuned for agentic workflows" and recommends it for new work. It costs $1.25 input and $4.25 output, accepts text, image, video, audio and PDF input, and has a 1,048,576-token window. Meta charges no long-context premium.

Best fit: Agentic pilots with long, mixed-media inputs.

Caveat: Meta says audio understanding in Muse Spark 1.3 is not fully supported. A Contributor variant costs $0.10 and $0.20, but check the data terms attached to that tier before sending it company content.

Which open-weight LLMs are worth testing?

Mistral Large 4, DeepSeek V4 Pro, Qwen3.6-27B and Muse Spark Open are the four open-weight models worth a pilot slot. Open weights let a team self-host and fine-tune. The licenses differ widely. Two use standard open-source licenses, and two add commercial conditions that a legal team should read before launch.

Mistral Large 4

Specifications: Mistral describes Large 4 as an open-weight, general-purpose multimodal model with 1.05T total and 52B active parameters and a 1M-token window. Mistral's API lists it at $0.70 input and $2.10 output.

Best fit: Teams that want a cheap hosted API now and the option to self-host later.

Caveat: The Mistral Ecosystem Open-Weight license is a modified MIT license. Businesses running a Model-as-a-Service offering must get a separate commercial license from Mistral before commercial use. Check whether your product falls into that group.

DeepSeek V4 Pro

Specifications: DeepSeek V4 Pro has 1.6T parameters (49B activated), a 1M-token window and an MIT license. DeepSeek's own API charges $1.32 per million input tokens on a cache miss and $3.96 for output at peak hours, and off-peak rates are half that.

Best fit: Teams that want the most permissive license at trillion-parameter scale and a hosted API for early tests.

Caveat: The model card calls the V4 series a preview. Peak hours run 01:00 to 04:00 and 06:00 to 10:00 UTC on weekdays, which overlaps the European working morning, and a 1.6T-parameter model needs a large GPU cluster to self-host.

Qwen3.6-27B

Specifications: Qwen3.6-27B has 27B parameters and an Apache-2.0 license. Its context is 262,144 tokens natively and extensible to 1,010,000.

Best fit: Agentic coding, including frontend workflows and repository-level reasoning, on a small fraction of the hardware the trillion-parameter models need.

Caveat: Going past 262K tokens needs YaRN, a method that stretches the model's position encoding to cover longer inputs. The model card warns that common frameworks apply static YaRN, which can hurt quality on shorter texts. Turn it on only for long-context jobs.

Muse Spark Open

Specifications: Meta released Muse Spark Open on 15 October 2026 as an 816B total, 42B active model with a 1M-token window. It is not served over Meta's Model API. Teams download the weights and run them on a runtime such as SGLang, vLLM, TensorRT-LLM or llama.cpp.

Best fit: Coding and agentic work where a team wants Muse Spark behaviour on its own infrastructure.

Caveat: The Muse Model License requires every product to prominently display "Built with Muse Spark Open". Once the licensee and its affiliates pass $20 million in gross revenue over 12 consecutive months, they must notify Meta and sign a separate agreement before offering the model as part of a Model-as-a-Service business. Check whether your product would count as such a service.

How do you choose between open and closed LLMs?

Pick a closed API when a fast pilot matters most and nobody on the team wants to run GPUs. Pick open weights when data must stay on your own infrastructure, when you need fine-tuning, or when volume grows large enough that per-token fees overtake hardware costs. Our view is that most teams should pilot one of each on the same tasks.

Fine-tuning now pushes harder toward open weights. OpenAI's API pricing page says the company is winding down its fine-tuning platform, which is no longer accessible to new users. Any plan that depends on tuning a model on company data should start from open weights or from a vendor that still offers tuning.

Retrieval matters as much as model choice in most business use cases. Test each candidate with retrieval augmented generation on your own documents, and fix the scoring rules before the first run. Arbisoft's generative AI team picks metrics such as accuracy, F1-score or recall per project. The same habit keeps a model shootout honest, because a model that writes fluent answers can still score poorly on the facts your users need.

What to expect from LLMs in 2027

Expect higher Google list prices and staged access to the newest frontier models. The points below are tied to announcements already made, and the forecasts in them are labelled as ours.

• Google prices rise. Google's Gemini API pricing page moves Gemini 4 Argon to $4 and $20 on 1 December 2026, and Gemini 3.8 Flash from $0.75 to $1.50 input and $3.75 to $7.50 output on 1 January 2027. Calls to the deprecated Gemini 2.5 Pro route to Gemini 3.8 Flash on 1 December 2026.
• Frontier models arrive in stages. Google calls Gemini 4 Carbon its strongest model, with a focus on cybersecurity and long-horizon tasks, and is releasing it first to a subset of cyber defenders through its Fairwind Program. Broader access has no date. Pricing starts at $2 and $10, then rises to $4 and $20 after an introductory period.
• The Claude line-up should hold through most of 2027. Anthropic commits to not retiring Fable 5.1 before 1 September 2027, Opus 5.5 before 22 September, Sonnet 5.5 before 28 September and Haiku 5.5 before 7 October 2027. Our advice is to schedule migration testing for the third quarter.
• Open-weight previews should settle. DeepSeek still labels V4 a preview, and Mistral opened the Large 4 weights in the same month as its earlier preview. We expect stable versions of today's previews during 2027, though no vendor has dated one.

Planning an LLM pilot

Shortlist one closed model and one open-weight model from the table, then run both on the same set of real tasks from your backlog before signing anything. Price the pilot at the scheduled new rates. That means Gemini at $4 and $20 and the long-prompt surcharges from OpenAI and xAI, because a December price change can reverse a cost comparison made in November. Have legal read each open-weight license before a customer-facing launch.

Most pilots stall at the point where the model meets company data. Arbisoft's Enterprise AI page says "We build pipelines and connectors to your actual systems of record," and that work is usually where a pilot succeeds or fails. To scope one, talk to the team about Enterprise AI services.

Frequently asked questions

What is the best LLM right now?

No single LLM is best for every task. As of November 2026, GPT-6 Astra and Claude Fable 5.1 are the top-priced flagships at $10 and $50 per million tokens, while Gemini 4 Argon is Google's flagship reasoning model. Anthropic recommends starting with the cheaper Claude Opus 5.5. Shortlist two or three models and test them on your own prompts.

Which LLM is cheapest?

GPT-6 Luna and Claude Haiku 5.5 are the cheapest models in this comparison, at $0.10 per million input tokens and $0.50 per million output tokens. Haiku 5.5 rises to $0.50 and $2.50 above 100K prompt tokens. Meta's Muse Spark Contributor tier costs less, at $0.10 and $0.20, but check its data terms before sending company content.

What is the best open-source LLM?

The answer depends on license and hardware. DeepSeek V4 Pro (MIT) and Qwen3.6-27B (Apache-2.0) carry the most permissive licenses, and Qwen runs on much smaller hardware at 27B parameters. Mistral Large 4 and Muse Spark Open are large open-weight options whose licenses add conditions for Model-as-a-Service businesses.

Which LLM has the largest context window?

The GPT-6 models list the largest window here at 1.05M tokens. Claude, Gemini 4 Argon, Muse Spark, DeepSeek V4 Pro and Mistral Large 4 sit at about 1M, and Grok 4.7 lists 500K. Qwen3.6-27B reaches 1,010,000 tokens only with YaRN scaling. Gemini 4 Argon's 1M limit covers input and output together.