Compare AI models
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | — | 21B |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | 1M | 128k |
| API pricingUSD per 1M tokens · lower wins · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $0.10 | — |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | $0.50 | — |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | $0.01 | — |
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | — | $0.018Darkbloom |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | — | $0.09Darkbloom |
| Benchmarks | ||
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | 45.9% | 10.9% |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | 57.4% | 17.3% |
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | — | 60.7% |
FrontierCode v1.1 (Main) · main splitAgentic coding — A set of very hard, frontier-difficulty coding tasks an AI agent has to complete end to end. The score is the share of tasks in the main split it solves. Higher is better. | 46.4% | — |
Terminal-Bench 4.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Version 4.0 recalibrated how much time, CPU and memory each task gets, removed eight tasks and fixed nineteen, so fewer runs fail for reasons that have nothing to do with the model. Scores are not comparable with earlier versions. Higher is better. | 39.2% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 71.5% |
MMLUGeneral knowledge — A 57-subject multiple-choice exam — history, law, medicine, maths — that was the standard measure of how much a model knows from 2020 until roughly 2024, when frontier scores crowded into the high 80s and labs moved on to harder tests. The scores here were published years apart under different testing setups, so read them as a historical record rather than a like-for-like ranking. Higher is better. | — | 85.3% |
OSWorld 2.1 (offline)Agentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Version 2.1, in its offline setting. Scores are not comparable with version 2.0. Higher is better. | 72.4% | — |
GDPval-AA v2.1Knowledge work — economically valuable knowledge work (v2.1, Crowd-BT Elo fit) | 1620 | — |
AA-Briefcase v1.1Knowledge work — Artificial Analysis agentic office-work eval (Elo, v1.1 rating fit) | 1578 | — |
Chartography · no toolsChart tasks — The same chart-centred test with no tools: the AI has to read each chart unaided. Scores run far lower than the with-tools version, so read the two as separate tests. Higher is better. | 46.4% | — |
| Overview | ||
| Company | Anthropic | OpenAI |
| Release date | Oct 7 2026 | Aug 5 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude Haiku 5.5vsGemini 4 Argongpt-oss-20bvsGemini 4 ArgonClaude Haiku 5.5vsMuse Spark 1.3gpt-oss-20bvsMuse Spark 1.3Claude Haiku 5.5vsGrok 4.7gpt-oss-20bvsGrok 4.7Claude Haiku 5.5vsDeepSeek-V4.1-Flashgpt-oss-20bvsDeepSeek-V4.1-FlashClaude Haiku 5.5vsMistral Large 4gpt-oss-20bvsMistral Large 4Claude Haiku 5.5vsKimi K3gpt-oss-20bvsKimi K3Frequently asked questions
Claude Haiku 5.5 leads gpt-oss-20b on 2 of the 2 benchmarks they both report (Humanity's Last Exam). Only Claude Haiku 5.5 has a verified first-party API price: $0.10 per million input tokens and $0.50 per million output tokens. No pay-as-you-go API rate is tracked for gpt-oss-20b. gpt-oss-20b shipped 428 days before Claude Haiku 5.5, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Claude Haiku 5.5) vs 128k (gpt-oss-20b). Claude Haiku 5.5 is closed, while gpt-oss-20b is open weight.
On Humanity's Last Exam · no tools, Claude Haiku 5.5 leads at 45.9% vs gpt-oss-20b at 10.9%. On Humanity's Last Exam · with tools, Claude Haiku 5.5 leads at 57.4% vs gpt-oss-20b at 17.3%.