Which LLM Should You Use? GPT-6.1 Sol, Opus 5.5, Argon and More (October 2026)

Six model releases landed between September 22 and September 30, 2026, and they moved the answer to "which LLM should I use" more than the previous five months did. Anthropic shipped Claude Opus 5.5 and OpenAI shipped GPT-6 Sol and Luna on September 22. Claude Sonnet 5.5 followed, OpenAI upgraded Sol to GPT-6.1 Sol on September 29, and Google announced Gemini 4 Argon on September 30, then opened it to paid API customers on October 13.

Availability is the detail most comparisons get wrong. Argon launched to a handful of trusted cyber defenders and became purchasable two weeks later, and a model you cannot sign up for is not a pick, however good its launch table looks. This guide separates what you can use today from what you can only read about, and every price in it carries a date.

Two more arrived in October: Mistral Large 4 on October 6 and Claude Haiku 5.5 on October 7.

What changed since 3 October 2026

Re-checked on 28 October 2026. The lineup holds, and one fact changed: Gemini 4 Argon went on sale on 13 October. On 9 October the guide gained two entries and a note.

• Changed on 28 October: Argon moved from invite-only to the paid API on 13 October. Its row, notes and the watch list now say so. No pick changed places, because nothing newer than the 3 October scores ranks a challenger higher.
• Added: a budget pick and a European option, both from the past week.
• Noted: one hosted-only model in the open-weight section.
• Not re-scored: every other row keeps its 3 October figures and date.

Latest LLM releases, as of 9 October 2026

Search interest in this topic is mostly recency ("latest llm releases", "current best llm"), so the newest items come first. Each entry says what shipped and whether you can use it. Prices sit in the table further down.

• October 7, Claude Haiku 5.5. Anthropic's cheapest current Claude, priced per Anthropic's pricing page by prompt length, with a step up once a prompt passes 100,000 tokens.
• October 6, Mistral Large 4. A public preview from the French lab. It says the weights follow by the end of October and has not named a licence, per its launch post.
• September 30, Gemini 4 Argon. The new top model has a 1M-token context window and an output limit raised from 64K to 1M tokens, per Google's announcement. It started with trusted cyber defenders through an invite-only program. On October 13 it opened to developers on the paid tier of the Gemini API, with the top consumer plan able to try it too.
• September 29, GPT-6.1 Sol. An upgrade to GPT-6 Sol that OpenAI says nearly matches GPT-6 Astra on agentic coding, computer use and professional work for a fifth of Astra's token price. It is in the API as gpt-6.1-sol and in Codex, but not yet in Chat.
• Late September, Claude Sonnet 5.5. Anthropic lists it at half the token price of Opus 5.5.
• September 22, Claude Opus 5.5. A 20% sticker cut from Opus 5, per Anthropic.
• September 22, GPT-6 Sol and GPT-6 Luna. Luna is the budget tier.

How to choose: four questions that split the field

Settle these before you open a pricing page. Starting from benchmark scores and leaving constraints for last is the usual way a small team gets this wrong.

• Must prompts stay on hardware you run? Then only open-weight models qualify, such as DeepSeek V4 or the newer GLM-5.3, and a proprietary API is off the table. The new European preview does not qualify yet, because nobody can download its weights today.
• Are you already paying for a Google or Microsoft seat? Workspace shops get Gemini bundled at the Business tier, and Microsoft shops get Microsoft Copilot the same way. Neither justifies switching platforms on its own.
• What is your monthly token volume? Under a few million tokens a month, sticker price barely matters. Past that, the gap between an open-weight rate and a frontier rate is a real line item on an invoice, not a rounding error.
• Is the task coding, writing, or research? Coding and structured output still favor Claude. Research that needs citations still favors Perplexity. Broad drafting and agent orchestration still favor ChatGPT.

A model that scores two points higher on a leaderboard is worthless if it cannot legally touch your data. The model leaderboard ranks every GA model by blended price alongside SWE-bench and GPQA, useful once your constraints, not the score, have narrowed the field. If you would rather answer four quick questions than read the rest of this guide, the model recommender does that filtering for you.

What it costs: the current lineup, one table

Prices move fast enough that a paragraph listing them goes stale within weeks. This table covers every model the guide discusses, current as of 3 October 2026, in dollars per million tokens, input then output, except the two newest model rows, which were checked on 9 October, and Argon, which was checked on 28 October.

Figures come from Anthropic's API pricing (the Haiku 5.5 rates from its current address, read on 9 October 2026), OpenAI's pricing page and Google's launch post; the Argon dates come from its model record. The rest trace to a vendor page or an aggregator that cites one.

Speed moved too. Anthropic says Opus 5.5 generates output more than 30% faster than Opus 5. Gemini 3.8 Flash is deliberately slower than its predecessor, with a 14.81-second p95 latency per llm-stats.com, because Google tuned it to spend more thinking tokens on hard problems.

What independent scores say about the same models

Sticker price is not task cost. Artificial Analysis runs every model through the same ten-evaluation Intelligence Index and reports what the run cost. Here is its table on 3 October 2026, at each model's highest listed effort setting. The Haiku 5.5 and Mistral Large 4 rows were read from their Artificial Analysis model pages on 9 October 2026.

Haiku 5.5 is verbose. The index run generated 440 million tokens from it, against a median of 100 million for comparable models, so a low per-token rate and a low cost per task are different numbers. The cheapest row above still costs less per task, at $0.07, but scores 38.

Read the effort settings before you compare rows. Argon is listed at high effort while the two Claude models are at max, so the real gap depends on the setting you would run.

Context windows are now mostly level, with 1M tokens listed for every frontier entry above. The difference is output length: Gemini 3.8 Flash caps output at 65,500 tokens, while Google's model page lists Argon at 65,536 by default and up to 1M with its long-decode mode. The newest preview is the exception: its maker lists 1M tokens, the independent index lists 524,000 for the live endpoint, so plan around the smaller figure.

The shortlist: Anthropic's new default, and its cheaper sibling

Opus 5.5 is the default this guide recommends for coding or long-form writing. Claude Pro costs $17 a month on the annual plan, per Anthropic's plans page. Max starts higher for 5x or 20x the usage.

Cache reads fell 60% with this release, to $0.20 per million tokens, which matters more than the headline rate for any agent that re-reads the same context. Anthropic's launch table credits Opus 5.5 with 66.4% on Terminal-Bench 4.0 where Opus 5 scored 52.3%, and 57.8% against 46.6% on CursorBench 4.0. Anthropic also puts typical running costs 40% below Opus 5's, because cache reads fall further than the sticker price.

Sonnet 5.5 is the value pick. It lists at half Opus 5.5's token price, and Artificial Analysis scores it 56 against 58. The caveat sits in the cost column above: at max effort it used enough tokens to cost more per task than Opus 5.5. Test your own workload at the effort setting you will actually run before assuming the cheaper sticker wins.

Anthropic's own model pricing table, the page to bookmark before assuming last month's rate still applies. Captured 23 Sep 2026, before Sonnet 5.5 was added to the table.

The shortlist: OpenAI and Google, priced the same week

GPT-6.1 Sol is OpenAI's answer to Opus 5.5, and "near-Astra" is OpenAI's own wording in its launch post, so read it as a vendor claim. OpenAI says Sol matches Astra on DeepSWE v1.1 and comes within 2.1 points of it on OSWorld 2.0 at maximum effort. On AutomationBench it says Sol beats Opus 5.5 by 2.2 points at medium effort for about a third of the cost.

Independent numbers are less dramatic and still favorable. Artificial Analysis puts Sol at 52 against Astra's 53, for $0.72 per task against $3.26. It trails Opus 5.5's 58. Both labs benchmark against the other's older model, which is why this guide leans on the third-party table.

The price details matter for Sol:

• Cached input costs $0.10 per million tokens.
• Prompts over 272K input tokens cost double on input and 1.5x on output for the whole request.
• Fast mode costs 2x the standard rate.
• Data residency covers the US and EU, and Fast mode is unavailable under EU residency.

OpenAI's Ultrafast tier, which it says runs up to 8x faster than standard, is priced today only for Astra, at six times Astra's standard rate. For Sol, OpenAI says Ultrafast arrives "in the coming days" and has published no price.

ChatGPT Plus is still $20 a month, with two Pro tiers above it that OpenAI split out in April. The caveat: Sol is in ChatGPT Work and Codex but not yet in Chat, so check which surface your team lives in before assuming the upgrade reached it.

Google's two models: Argon for the hard jobs, Flash for the cheap ones

Argon is now Google's top model, so Gemini 3.8 Flash can no longer stand in for Google's best. Google says Argon sets a new state of the art on DeepSWE v1.1 at 77.9% and ranks first on AutomationBench at 51.3%. Artificial Analysis scores it 53, level with Astra and below Opus 5.5.

Argon is on sale now, which changes what you can do with that, not who leads. It earns a place on the test list for long-horizon coding and very long outputs. It is not a new default.

The 53 is a 3 October reading at high effort, while the Claude models were scored at max. The introductory rate ends on 30 November per its HokAI record, and the table above carries both rates. The model page lists it as a stable release with batch and flex inference supported.

Gemini 3.8 Flash is the cheaper Google option. According to llm-stats.com, it edges Claude Opus 5, not 5.5, on Terminal-bench 2.1 (89.4% versus 85.8%) and OSWorld-2.0 (59.0% versus 50.6%). Artificial Analysis scores it 41, well below the frontier group. The caveat: its rate holds only through December 31, 2026, after which both prices double.

Google is still the only one of the three labs whose API pricing page leads with a genuinely free tier. The trade-off is printed at the bottom of the card: content is used to improve their products. Captured 17 Aug 2026.

The shortlist: a budget pick and a European option

Claude Haiku 5.5 is the budget pick from a major lab. Anthropic prices it by prompt length, with the rates in the table above and a higher rate once a prompt passes 100,000 tokens, per its pricing page on 9 October 2026. It is the one current Claude without a flat rate across the full 1M-token window. Its vendor positions it for high-volume, latency-sensitive work such as classification, extraction and routing.

Three caveats apply:

• The tokenizer counts more. The vendor says a given text now costs around 30% more tokens to send, so the saving against the older Haiku's $1 and $5 is smaller than the headline ratio.
• It is verbose. Budget with $0.21 per task from the table, not the per-token rate.
• It is not the hard-coding model. Its 43 on the independent index sits 15 points behind Opus 5.5's 58, so keep it for high-volume jobs and send difficult coding to a larger model.

A separate guide covers how it compares with its predecessor, and the full lineup is in which Claude model to use.

Mistral Large 4 is the option for teams that want a European lab. It launched as a public preview and scores 38 on the independent index at $1.13 per task, priced at list rates. The company's own pricing page shows half that as a sale and states no end date.

The open questions are the ones that matter for self-hosting. The weights are promised by the end of October and no licence is named, so until they ship it behaves like any hosted API. Do not pick it yet for the control reasons in the first section. Our guide to what the model is for covers the three jobs it suits, and the cheapest LLM API in 2026 puts it next to the other low-cost options.

The shortlist: open-weight options

DeepSeek V4 remains a safe self-hosted default. HokAI's own tracking lists it as generally available and its Flash checkpoint as preview, which matters if you are building a production pipeline rather than a prototype. Both checkpoints are MIT-licensed.

The caveat: those checkpoints date from April, and Artificial Analysis now lists newer DeepSeek entries, including DeepSeek V4.1 Flash at an index of 39. Check which checkpoint you would deploy.

Z.ai's GLM-5.3 is the open-weight model to test first. Artificial Analysis lists an August 2026 open-weights release with an index of 45 and a cost of $2.01 per task. It ships under its own "GLM-5.3 License" per that page, so read the terms before assuming the unrestricted MIT license its predecessor carried.

Its predecessor, GLM-5.2, launched June 13, 2026 at the same listed price, per go-to-agency.com. If you already use Z.ai and are weighing a move, our Z.ai alternatives guide compares its coding plan with rivals' and prices GLM-5.3 against other open models.

GLM-5.2 shipped without a full official benchmark suite, per Eigent, so use the independent score for GLM-5.3 and test both on your own workload.

Ling 3.1 Flash scores 41 on the independent index, but it is a hosted API with no downloadable weights, so it answers a price question and not a self-hosting one. Its launch trial was free, and its maker described the free period as two weeks.

Claude versus GPT-6.1 Sol: the decision most teams are actually making

Most of the traffic to a guide like this is really one question: Claude or GPT, for a small team, right now.

For a two-person team shipping an AI feature and needing the widest third-party library support, Sol is the more defensible default. It costs a fraction of what Astra charges, it scores 52 against Astra's 53 on the independent index, and OpenAI's ecosystem is still the largest of any provider.

For a four-person engineering team that lives inside an AI coding tool running Claude models, Opus 5.5 is difficult to beat on the numbers above. It tops the independent index. Neither is a clean sweep, and for latency-sensitive inference the hosting choice often matters more than the model behind it.

A short test settles most of these disputes better than any table. Take twenty real tasks from the last month, such as a pull request to review, a customer email to answer and a document to summarize. Run each through your two or three finalists at the effort setting you would pay for in production.

Record the output quality, the tokens used and the time taken, then multiply by your monthly volume. Teams that do this once usually find that one task type decides the bill, and that the cheapest sticker price is rarely the cheapest result.

This guide covers the model layer only. If the decision in front of you is which coding tool to run a model inside, Best AI Coding Assistants in 2026 breaks the job down by editor, agent autonomy and repo size. If the question is which chat product to open by default, Best AI Chatbots in 2026 ranks the consumer apps, and How to Choose an AI Assistant covers the subscription choice.

Data handling: what each lab does with a prompt

This is the question procurement asks before the pricing question, and it gets skipped in most comparisons. Read each lab's current data terms before pasting client material into any of them, because defaults differ by plan.

Anthropic's plan comparison lists model training as an opt-out setting. OpenAI says Sol supports US and EU data residency in the API. Google's free consumer tier uses content to improve its products, a trade-off printed at the bottom of its own pricing card.

Open-weight models run through their vendors' hosted APIs, rather than self-hosted, carry the standard sourcing question some procurement teams ask about any model whose weights and training data originate outside their jurisdiction. Self-hosting the open weights removes that question entirely, which is the real reason a regulated team picks an open-weight model over a hosted API: control, not price. This guide could not verify a formal data-processing addendum from either vendor; raise it with them directly before a regulated deployment.

Who should skip this entire exercise

If your team has three chat subscriptions active and cannot name a specific task each one covers that the others do not, that is not redundancy for safety. It is a subscription problem. Cancel two, keep the one your team opens daily, and revisit in three months.

If your main need is sourced, current research rather than generation, a single research-focused assistant covers most of the job without a frontier chat subscription.

And if your team is under three people and nobody is building an AI-native product, a single $20-a-month plan, used consistently, beats a carefully optimized model stack that gets opened twice a week. Optimization only pays off once usage is high enough for the per-token gap to show up on an invoice.

Teams that cannot tell which side of that line they are on can run their actual workload through Smart Match rather than guess from a table of list prices. How Smart Match works explains what it asks and how it ranks.

Once a team outgrows individual subscriptions, the math changes again. Claude's Team plan lists a Standard seat at $20 a month billed annually and a Premium seat with 5x the usage at $100, per Anthropic's plans page.

Neither is worth centralizing billing for at four people. Both start paying for themselves at ten or more seats, when one invoice beats four personal receipts and someone tracks per-seat usage closely enough for the difference to show up in a monthly finance review.

Two stacks that cover most teams

1. Solo builder or two-person startup. One primary subscription plus one gap-filler, not four: Claude Pro for coding and written output, plus one research tool for anything needing a current, sourced answer.
2. Four-person engineering team with real API spend. Route routine, high-volume calls through an open-weight model or Claude Haiku 5.5, and reserve Opus 5.5 or Sol for requests where instruction-following or agent tool use actually matters. At 50 million tokens a month, splitting the load instead of routing everything through a flagship is thousands of dollars, not a rounding error.

Neither stack should run four subscriptions in parallel. If you cannot name the specific job each tool does that the others cannot, you are paying for redundancy, not coverage.

The turn: convergence versus the calendar

The strongest objection to reshopping every few months is that model quality has converged enough that switching costs, not capability, now decide the question. The independent numbers support it. On Artificial Analysis the top five entries sit between 52 and 58 on the index, while their cost per task runs from $0.72 to $7.67, more than a tenfold spread for a six-point quality gap.

That argument mostly holds, which is why a team that picked Claude Sonnet 5 in July has no urgent reason to migrate. The exception is a team whose bill is dominated by one workload. For them a gap that large is worth an afternoon of testing, because the cheaper model is sometimes the better one for that job and sometimes, as Sonnet 5.5's task cost shows, not.

What would change this answer before the end of 2026

Watch four things. First, what Argon costs once its introductory price ends. At $4 and $20 from 1 December it carries the same sticker as Opus 5.5, which scored 58 against its 53 on the 3 October index, so the case for it has to rest on the long-output work rather than on price.

Second, whether OpenAI's Ultrafast tier reaches Sol, and at what price. Today only Astra has a published Ultrafast rate.

Third, whether Gemini 3.8 Flash's pricing holds past December 31, when both its rates are scheduled to double. Fourth, whether the French lab ships its promised weights by the end of the month, and under which licence. The answer decides whether it joins the open-weight shortlist or stays a hosted preview.

Only the first has a date on it, and none has resolved yet. Any one of them is enough to make this guide wrong again before the calendar turns.

That is the nature of a market where six models can ship in nine days, and where a model that was gated at launch can be on sale two weeks later. Bookmark the pricing pages, not the conclusion.

Frequently asked questions

Which LLM should I use in 2026?

For most small teams doing coding or writing, Claude Opus 5.5 is the strongest default as of October 2026, at $4 input and $20 output per million tokens and 58 on the Artificial Analysis index. GPT-6.1 Sol is the cheaper alternative at $2 and $10, and Claude Haiku 5.5 is the budget pick at a score of 43 and $0.21 per task.

What are the latest LLM releases?

Between September 22 and October 7, 2026: Claude Opus 5.5, GPT-6 Sol and Luna, Claude Sonnet 5.5, GPT-6.1 Sol, Gemini 4 Argon, Mistral Large 4 (preview) and Claude Haiku 5.5. All can be used today, though Mistral Large 4 is a preview and Argon opened to paid API customers only on October 13.

Is GPT-6.1 Sol as good as GPT-6 Astra?

OpenAI says it nearly matches Astra on agentic coding and computer use at one-fifth of the token price. Artificial Analysis scores it 52 against Astra's 53 at max effort, at $0.72 per task against $3.26. Astra still leads on the hardest science tasks, per OpenAI.

Can I use Gemini 4 Argon yet?

Yes, since October 13, 2026. Google opened it to developers on the paid tier of the Gemini API, and Google AI Ultra subscribers can try it in AI Studio and Antigravity. Its price is $2 and $10 per million tokens through 30 November, then $4 and $20.

Can a self-hosted model match Claude Opus 5.5?

Not on the independent index. Artificial Analysis scores the open-weight GLM-5.3 at 45 and DeepSeek V4.1 Flash at 39, against 58 for Opus 5.5. The gap is real, but so is the price difference, so test your own workload before paying for a frontier model. Mistral Large 4 cannot be self-hosted yet: its weights are promised for the end of October, with no licence named.

Covered in this guide

• Claude: Claude is Anthropic's AI assistant, with a free tier, paid personal plans, and Team and Enterprise seats. Opus access starts at the first paid plan.
• ChatGPT: ChatGPT is OpenAI's AI assistant, with 1.2 billion weekly users by OpenAI's count, the GPT-6 model family and plans that run from a free tier to a premium Pro tier.
• Claude Haiku 5.5: Anthropic's smallest and fastest 5.5 model, released October 7, 2026, with a 1M context window, built for high-volume work.
• Claude Opus 5.5: Anthropic's September 2026 flagship LLM with a 1 million token context window, built for long-running agentic coding and computer use.
• Claude Sonnet 5: Claude Sonnet 5, released by Anthropic on June 30, 2026, hits 82.1% SWE-bench Verified with a 1M-token context window.
• Claude Sonnet 5.5: Anthropic's fastest Sonnet-class model, launched in September 2026, with a 1M context and output more than 30% faster than Sonnet 5.
• Cursor: Cursor is an AI code editor built on VS Code, widely adopted across large enterprise engineering teams, with Agent Mode, Tab completion, and Cloud Agents.
• DeepSeek V4: DeepSeek V4-Pro reached GA in August 2026 under MIT, matching frontier models on agentic coding. Open-source MoE, 1M-token context, peak/off-peak API pricing.
• DeepSeek V4 Flash: DeepSeek-V4 Flash: 284B-param MoE with 13B active (April 2026), 1M context, 79% SWE-bench, $0.14/M input. Open-source MIT license.
• DeepSeek-V4.1-Flash: The MIT-licensed vision-and-text model, launched in September 2026 and refreshed with an October snapshot.
• Gemini: Google's multimodal AI assistant and model family, sold as a free app, tiered subscriptions and a pay-per-token API
• Gemini 3.8 Flash: Google DeepMind shipped Gemini 3.8 Flash on September 2, 2026, a multimodal Gemini 3 model tuned for long-horizon coding and agentic enterprise work.
• Gemini 4 Argon: Google DeepMind's Gemini 4 frontier model, generally available on the paid Gemini API: 1M-token context and output, text, image, video, audio and PDF input.
• GLM-5.2: GLM-5.2 is Z.ai's June 2026 open-source 744B MoE model with a 1M-token context window, 85.6% GPQA Diamond (Vals AI), and MIT license at $1.40/1M input tokens.
• GLM-5.3: GLM-5.3 is Z.ai's text-only flagship for agentic coding and security work, with a 1M-token context, a live API and open weights.
• GPT-6 Astra: OpenAI's flagship model, launched September 2026 as the first ever rated at the Preparedness Framework's Critical cybersecurity level.
• GPT-6 Luna: GPT-6 Luna (Sept 2026) is OpenAI's lowest-cost GPT-6 model, tuned for high-volume chat, extraction and classification with six adjustable reasoning-effort levels.
• GPT-6 Sol: OpenAI's mid-tier GPT-6 model (Sep 2026), 1.05M-token context, 68.8% on DeepSWE v1.1 coding, half GPT-5.6 Sol's price.
• GPT-6.1 Sol: GPT-6.1 Sol is OpenAI's September 2026 mid-tier reasoning model for coding, computer use and document work, built to sit just below GPT-6 Astra.
• Groq: Fast open-model inference, now built on NVIDIA-licensed LPU hardware
• Hugging Face: The AI community building the future. Platform for discovering, sharing and collaborating on machine learning models, datasets and applications.
• Ling 3.1 Flash: InclusionAI's text-only reasoning model for coding agents and long tasks, released on 30 September 2026 as the successor to InclusionAI's July flash model.
• Meta AI: Meta's free assistant inside Facebook, Instagram, WhatsApp and Messenger, powered by Muse Spark, with Meta One bundles for heavier image and video generation.
• Microsoft Copilot: Your everyday AI companion powered by GPT-5.6 (preferred model July 2026), GPT-4o, and GPT-5 for productivity, creativity, and intelligent assistance
• Mistral AI: Paris lab behind Mistral Large 4 (1T parameters, preview Oct 2026) and Vibe, Studio, Forge and Compute. Raised a €3B Series D at over €21B in Sept 2026.
• Mistral Large 4: Mistral Large 4 is Mistral AI's largest open-weight multimodal MoE model, launched 6 Oct 2026 in public preview with image input.

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