amazon-nova-experimental-chat (26-02-10)
Made by Amazon. Epoch AI does not state how it can be accessed. No Top tier or Strong results yet among the models listed here.
In short
What it does well
No Top tier or Strong ratings on the four main tests yet.
Where it falls short
- Writing: Behind the leaders (72nd of 77 listed models)
- Coding: Middle (42nd of 77 listed models)
What we don't know yet
- Web-building votes: Not enough data
- Hard maths problems: Not enough data
- School-competition maths: Not enough data
- Graduate-level science questions: Not enough data
- Short factual questions: Not enough data
- Fixing real software bugs: Not enough data
- Difficult coding tasks: Not enough data
The four ratings
- Top tier
- Strong
- Middle
- Behind the leaders
- Not enough data
Ratings compare only the 77 models listed here, in quarters, best first. “Not enough data” means no result yet. It is never a low score.
- Writing
- Behind the leadersHow often people preferred its answers to writing prompts. 1,369 on Arena's scale · Source for amazon-nova-experimental-chat (26-02-10) writing score (lmarena)
- Coding
- MiddleHow often people preferred its answers to coding questions. 1,483 on Arena's scale · Source for amazon-nova-experimental-chat (26-02-10) coding score (lmarena)
- Maths
- Not enough dataNo result yet for hard maths problems.
- Knowledge
- Not enough dataNo result yet for short factual questions.
Evidence completeness: Thin evidence, 6 of 13 tests have a result.
Where it is relatively stronger or weaker
This compares each kind of request with the model's own overall position among the 77 listed models, using Arena's votes. It is about performance on different languages, kinds of request and topics, not about political or social bias.
Relatively stronger
- Expert-level prompts: 20th of 77 on prompts that need expert knowledge, against 46th of 77 overall. Source for Expert-level prompts
Relatively weaker
- Chinese: 69th of 76 on questions asked in Chinese, against 46th of 77 overall. Source for Chinese
- Entertainment, sport and media: 69th of 77 on entertainment, sport and media questions, against 46th of 77 overall. Source for Entertainment, sport and media
- Russian: 66th of 77 on questions asked in Russian, against 46th of 77 overall. Source for Russian
Show every category we can see
| Category | Position | Arena score | Votes | Source |
|---|---|---|---|---|
| Expert-level prompts Kind of request | 20th of 77 | 1,506 95% range 1,474 to 1,538 | 304 | Source for Expert-level prompts |
| Hard prompts Kind of request | 48th of 77 | 1,459 95% range 1,446 to 1,471 | 2,031 | Source for Hard prompts |
| Long prompts Kind of request | 57th of 77 | 1,441 95% range 1,424 to 1,459 | 1,022 | Source for Long prompts |
| Longer conversations Kind of request | 62nd of 77 | 1,442 95% range 1,418 to 1,465 | 615 | Source for Longer conversations |
| Chinese Language | 69th of 76 | 1,464 95% range 1,433 to 1,496 | 316 | Source for Chinese |
| English prompts Language | 42nd of 77 | 1,458 95% range 1,443 to 1,474 | 1,311 | Source for English prompts |
| Non-English prompts Language | 51st of 77 | 1,435 95% range 1,423 to 1,447 | 2,212 | Source for Non-English prompts |
| Russian Language | 66th of 77 | 1,423 95% range 1,399 to 1,447 | 495 | Source for Russian |
| Entertainment, sport and media Topic | 69th of 77 | 1,397 95% range 1,374 to 1,420 | 571 | Source for Entertainment, sport and media |
| Science Topic | 59th of 77 | 1,453 95% range 1,430 to 1,476 | 549 | Source for Science |
| Software and IT Topic | 41st of 77 | 1,481 95% range 1,465 to 1,496 | 1,301 | Source for Software and IT |
Bias: what we can and can't see
Our sources do not measure political lean, stereotyping, flattery (agreeing with you too readily) or safety behaviour, so we cannot say how any model does on those. What we can show is above: where a model is relatively stronger or weaker across languages, kinds of request and topics. Also, people voting in Arena can favour answer style, such as length and formatting, and our numbers do not adjust for that. See what AI bias means and what these tests can tell you.
Best-supported use cases
- Coding and building things: not ranked, because there is not enough data for the main test.
- Homework and maths: not ranked, because there is not enough data for the main test.
- Research and checking facts: not ranked, because there is not enough data for the main test.
Where an alternative might be more suitable
- Writing and creating content: rated Behind the leaders on writing votes. Compare it with Claude Opus 5.5, the best-supported choice for this task.
- Coding and building things: rated Middle on coding votes. Compare it with Claude Opus 5.5, the best-supported choice for this task.
- Homework and maths: no result on hard maths problems, so we cannot say. See which models the evidence does support.
- Business emails and everyday work: rated Strong on business-task votes. Compare it with Gemini 4 Argon, the best-supported choice for this task.
- Research and checking facts: no result on short factual questions, so we cannot say. See which models the evidence does support.
What it is
- Maker
- Amazon
- Access, as listed by Epoch AI
- Not stated
- Licence, as listed by Arena
- Proprietary
- Last observation in our data
- 28 Oct 2026
- Data last changed
- 29 Oct 2026, 14:36 UTC
We have not verified an official product page for this model, so we do not link to one.
How you can use it
Proprietary. The model's files are not published, so it is generally available only through its maker or partners the maker chooses.
How you can reach it (an app, a website, a developer API or a third-party tool) and what that costs are not covered by our benchmark data. Where we could verify prices from the maker's own pages, they are shown under Price. See models, apps and harnesses and how AI pricing works.
Price
Price not independently verified
We have not been able to check this maker's own pricing page, so we show nothing for it.
We never infer subscription prices from API prices. New to “tokens” and “per million”? See how AI pricing works.
Good to remember
- Benchmarks are samples of tasks. None proves performance in every situation.
- Arena scores come from people choosing between answers. That measures preference, not correctness.
- Ratings are relative to the 77 models listed here. A different set would change them.
- Sources test models at different settings. Each result shows the setting used, and we do not adjust it.
The evidence, for the curious
Show all 13 tests with settings and sources
| Test | Result | Rating | Setting used | Source identifier | Observed | Links |
|---|---|---|---|---|---|---|
| Arena text: overall preference Arena 'text' leaderboard (Bradley-Terry rating, no style control) |
1,449 Arena score 95% range 1,439 to 1,458, 3,523 votes |
Middle | effort: 26-02-10, bradley-terry rating | amazon-nova-experimental-chat (26-02-10) lmarena · text/latest |
28 Oct 2026 | Source for amazon-nova-experimental-chat (26-02-10) Arena text: overall preference score (lmarena) |
| Arena text: creative writing Arena 'text' leaderboard, category creative_writing |
1,369 Arena score 95% range 1,343 to 1,394, 482 votes |
Behind the leaders | effort: 26-02-10, bradley-terry rating | amazon-nova-experimental-chat (26-02-10) lmarena · text/latest |
28 Oct 2026 | Source for amazon-nova-experimental-chat (26-02-10) Arena text: creative writing score (lmarena) |
| Arena text: coding Arena 'text' leaderboard, category coding |
1,483 Arena score 95% range 1,464 to 1,502, 883 votes |
Middle | effort: 26-02-10, bradley-terry rating | amazon-nova-experimental-chat (26-02-10) lmarena · text/latest |
28 Oct 2026 | Source for amazon-nova-experimental-chat (26-02-10) Arena text: coding score (lmarena) |
| Arena text: maths Arena 'text' leaderboard, category math |
1,437 Arena score 95% range 1,399 to 1,476, 209 votes |
Behind the leaders | effort: 26-02-10, bradley-terry rating | amazon-nova-experimental-chat (26-02-10) lmarena · text/latest |
28 Oct 2026 | Source for amazon-nova-experimental-chat (26-02-10) Arena text: maths score (lmarena) |
| Arena text: following instructions Arena 'text' leaderboard, category instruction_following |
1,427 Arena score 95% range 1,409 to 1,445, 995 votes |
Middle | effort: 26-02-10, bradley-terry rating | amazon-nova-experimental-chat (26-02-10) lmarena · text/latest |
28 Oct 2026 | Source for amazon-nova-experimental-chat (26-02-10) Arena text: following instructions score (lmarena) |
| Arena text: business and management Arena 'text' leaderboard, category industry_business_and_management_and_financial_operations |
1,450 Arena score 95% range 1,428 to 1,472, 636 votes |
Strong | effort: 26-02-10, bradley-terry rating | amazon-nova-experimental-chat (26-02-10) lmarena · text/latest |
28 Oct 2026 | Source for amazon-nova-experimental-chat (26-02-10) Arena text: business and management score (lmarena) |
| Arena WebDev: building web apps | Not enough data | |||||
| FrontierMath Tiers 1-3 (v2) | Not enough data | |||||
| OTIS Mock AIME 2024-2025 | Not enough data | |||||
| GPQA Diamond | Not enough data | |||||
| SimpleQA Verified | Not enough data | |||||
| SWE-bench Verified (Epoch runs) | Not enough data | |||||
| FrontierCode (reported by Cognition) | Not enough data | |||||
Missing evidence
Not enough data for these tests:
- Arena WebDev: building web apps
- FrontierMath Tiers 1-3 (v2)
- OTIS Mock AIME 2024-2025
- GPQA Diamond
- SimpleQA Verified
- SWE-bench Verified (Epoch runs)
- FrontierCode (reported by Cognition)
Missing results are not low scores and do not count against the model.
Where a source gives no public run log, we cannot show the run behind the score. See data sources.
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