what is the best ai model for coding in october 2026?
as of october 29, 2026, our pick for the best ai model for coding is claude opus 5.5 in claude code for long, open-ended agentic work, and claude sonnet 5.5 or gpt-6.1 sol for everyday coding at a lower price. for cheap background agents, use claude haiku 5.5 or gpt-6 luna. for local work, qwen3 coder next is the one to try.
download the remynd desktop app so whichever model wins next month can start from a searchable record of what you worked on.
we first published this page in september and rewrote it from the vendor pages below, all checked on the same day. every number names who measured it.
what shipped since late september?
a lot shipped in five weeks:
- september 28: anthropic released claude sonnet 5.5.
- september 29: openai's gpt-6 astra post gained an update pointing to gpt-6.1 sol, now in chatgpt work and codex for plus, pro, business, enterprise and edu users but, per openai, not yet in chatgpt chat.
- october 7: anthropic released claude haiku 5.5, built for subagents and other high-volume work.
- october 12: meta released muse wonder, per its model card.
- october 13: google made gemini 4 argon globally available to developers through its api.
- october 28: google announced gemini 4 carbon, starting with a subset of cyber defenders rather than general availability.
deepseek also now serves deepseek v4.1 flash 1017 behind its flash api name. for a deeper look, see is deepseek v4.1 flash 1017 good for coding? and muse wonder vs claude opus 5.5.
how do the models compare on price and benchmarks?
| model | api price, in / out per 1m tokens | terminal-bench 4.0, as the vendor reports it | deepswe v1.1, as the vendor reports it |
|---|---|---|---|
| claude sonnet 5.5 | $2 / $10 | 70.6% (max effort) | not reported |
| claude opus 5.5 | $4 / $20 | 66.4% (xhigh effort) | not reported |
| gpt-6 astra | $10 / $50 | 57.9% (openai's best at any effort) | 74.1% (openai's best at any effort) |
| gpt-6.1 sol | $2 / $10 | not reported | matches astra, per openai |
| gemini 4 argon | $2 / $10 through november 30, 2026, then $4 / $20 | google self computes one; not used here, see the next table | google self computes one; not used here |
| grok 4.7 | from $2 / $6 | 37.6% (xhigh effort) | 71.0% (high effort) |
| deepseek v4.1 flash 1017 | $0.30 / $1.20 at peak hours (input on a cache miss), half that off-peak | 42.3% (claude code harness) | 77.0% (mini swe v2 harness) |
| claude haiku 5.5 | $0.10 / $0.50 for prompts up to 100k tokens | 39.2% (max effort) | not reported |
| gpt-6 luna | $0.10 / $0.50 | not reported here | not reported here |
| gemini 3.8 flash | $0.75 / $3.75 through december 31, 2026 | not reported here | not reported here |
| muse wonder | $4 / $20 | 52.0% (artificial analysis run, per meta) | 78.8% (datacurve run at max effort, per meta) |
sources: prices from claude pricing, openai's gpt-6.1 sol post, gemini api pricing, xai's grok 4.7 post, deepseek's pricing page and meta's muse wonder developer post. benchmark scores come from anthropic's opus 5.5 page (opus 5.5, with astra's score as openai reported it), anthropic's sonnet 5.5 and haiku 5.5 pages, openai's astra post, xai's grok 4.7 post, meta's muse wonder post and deepseek's model card.
read the columns loosely. each vendor picks its own harness and effort level, and deepseek's card names two different harnesses for its two scores.
is there one scorer that ran them all the same way?
for a handful of frontier models, yes. google's gemini page shows a terminal-bench 4.0 row marked (aa), and its carbon methodology says those results "are sourced from artificial analysis."
| model | terminal-bench 4.0, artificial analysis run shown by google |
|---|---|
| gemini 4 carbon | 65.2% |
| claude opus 5.5 | 59.6% |
| gpt-6 astra | 59.1% |
| gemini 4 argon | 57.1% |
| claude fable 5.1 | 52.0% |
two things stand out. first, opus 5.5 scores lower here than in anthropic's own table, a reminder that the scorer and the effort level move the number. second, google's best scorer in this table is carbon, which most developers cannot use yet. in our read, among models you can call today, opus 5.5 and astra sit close together, and argon is not far behind.
claude code vs codex vs antigravity: does the harness matter?
in our view, more than most rankings admit: the model is half the product. the other half is the loop around it: how it plans, reads files, runs tests and recovers.
claude code runs the claude family. anthropic says sonnet 5.5 at max effort "even performs comparably to opus 5.5" on several evaluations, but that opus 5.5 stays stronger on open-ended work. use sonnet by default and switch up when a task needs sustained judgment.
codex now has gpt-6.1 sol. openai says sol matches astra on deepswe v1.1 "at roughly one-fifth of the cost", which in our view makes it the everyday pick on the openai side.
google's stack gets argon through the gemini api and, for google ai ultra subscribers, in antigravity. argon is not on the gemini api free tier, and its introductory price has an end date.
grok build is xai's command line coding tool, and xai says grok 4.7 is available in it and in cursor.
what is the best open weight coding model?
three stand out, for different reasons.
deepseek v4.1 flash 1017 posts the highest deepswe v1.1 score of the open weight models here, though that is its maker's own count (see the table above). it ships under mit with about 763b parameters, so it is a datacenter model. deepseek also serves it through its own api.
glm-5.3 from z.ai reports 66.9% on deepswe v1.1 on its model card and has about 753b parameters. it is also a datacenter model.
qwen3 coder next is the local coding model to try. it has 80b total parameters but activates only 3b per token, ships under apache 2.0, and reports 70.6% on swe-bench verified. qwen says the sparse design makes it cost-effective to run, but all its weights still need memory.
the verdict, by job
- large refactors and open-ended agentic work: claude opus 5.5 in claude code. anthropic says it stays stronger than sonnet 5.5 on open-ended work, and it edges astra and argon in the artificial analysis table.
- everyday coding and pairing: claude sonnet 5.5 or gpt-6.1 sol, both at the same list price. pick whichever cli you already like.
- google's stack: in our view, try gemini 4 argon while the introductory price lasts; see is gemini 4 argon available? for google's own scores.
- cheap subagents and background jobs: claude haiku 5.5 and gpt-6 luna are the cheapest coding model options in the first table, fine for simple tasks. gemini 3.8 flash is the google option.
- running locally: qwen3 coder next. accept a lower ceiling in exchange for no api bill.
- self hosting at team scale: deepseek v4.1 flash 1017 or glm-5.3, if you have the gpus.
our view: if you only change one thing this month, move your default from opus to sonnet 5.5 or sol and keep opus for the hard tasks.
what this page cannot tell you
- vendor numbers are vendor numbers. most scores above come from launch posts. only the second table shares one scorer, and it covers five models.
- benchmarks are not your repo. anthropic itself says benchmark margins have become "a less reliable guide to real-world differences."
- prices change. argon and gemini 3.8 flash both list introductory prices with end dates, and deepseek charges different peak and off-peak rates.
- this is a snapshot. the next launch will likely reorder it.
why the model is rarely the bottleneck
here is the part the leaderboards leave out. in our view, a coding agent fails less often because it cannot write code, and more often because it does not know what you were doing.
it cannot see the stack overflow thread you read yesterday, the slack message where the api shape was agreed, or the error you already chased for two hours. we covered this in giving an ai coding agent memory of your actual work and in what is context engineering. a bigger context window does not help if nothing useful goes in it.
remynd records your screen on a mac, runs ocr locally with apple vision, and keeps the searchable index on your mac. you can exclude apps and sites. press control space, search in plain language, and jump to the exact moment something was on screen.
remynd also runs on windows 11 (x64 or arm64).
its agent window runs your own claude code or codex cli against that history, with read-only access. connected agents send what they read to their own cloud, so treat them like any other cloud tool. switch from opus 5.5 to gpt-6.1 sol next month and the same history is still there to search. more in private ai on your mac.
download the remynd desktop app and let whichever model wins next month start with what you already know.