DeepSeek-V4.1-Flashvsgpt-oss-20b
DeepSeek-V4.1-Flash | gpt-oss-20b | |
|---|---|---|
| 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. | — | 128k |
| API pricingUSD per 1M tokens · lower wins | ||
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. | — | $0.02AkashML |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | — | $0.10AkashML |
| 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. | 36.8% | 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. | 63.9% | 17.3% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 90.9% | 71.5% |
| BenchmarksPublished by one model only | ||
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% |
DeepSWE 1.1Agentic coding — Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | 74.2% | — |
NL2Repo-BenchRepo-level code generation — Tests whether the AI can turn a natural-language requirement into working code across an entire repository, not just produce a single function or patch. Higher is better. | 65.4% | — |
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. | 31.2% | — |
Terminal-Bench 3.0Agentic terminal coding — command-line task completion (v3.0, much harder task set) | 30% | — |
Terminal-Bench 2.1Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | 90.6% | — |
CyberGymCybersecurity — Tests the AI on cybersecurity challenges — finding and exploiting software weaknesses inside a safe sandbox. Higher is better. | 88.1% | — |
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% |
Agent's Last Exam · pass@1Agentic computer use — A hard set of desktop and operating-system tasks an AI agent has to finish by looking at the screen and working the machine itself. The score is the share it passes outright — partial credit does not count. Higher is better. | 31.8% | — |
AutomationBenchBusiness workflows — Tests whether the AI can run real multi-step business workflows — the kind of end-to-end office processes companies want to automate — from start to finish. Higher is better. | 54.8% | — |
Chartography · with toolsChart tasks — A chart-centred test run with tools available to the AI, reported separately from the chart-reading benchmarks above it. Higher is better. | 78.9% | — |
| Overview | ||
| Company | DeepSeek | OpenAI |
| Release date | Sep 10 2026 | Aug 5 2025 |
| Access | Proprietary | Open Weight |
Other comparisons
DeepSeek-V4.1-FlashvsClaude Fable 5.1gpt-oss-20bvsClaude Fable 5.1DeepSeek-V4.1-FlashvsGemini 3.8 Flashgpt-oss-20bvsGemini 3.8 FlashDeepSeek-V4.1-FlashvsMuse Spark 1.3gpt-oss-20bvsMuse Spark 1.3DeepSeek-V4.1-FlashvsGrok 4.6gpt-oss-20bvsGrok 4.6DeepSeek-V4.1-FlashvsMistral Medium 3.5gpt-oss-20bvsMistral Medium 3.5DeepSeek-V4.1-FlashvsKimi K3gpt-oss-20bvsKimi K3
Frequently asked questions
DeepSeek-V4.1-Flash leads gpt-oss-20b on 3 of the 3 benchmarks they both report (Humanity's Last Exam, GPQA Diamond). gpt-oss-20b shipped 401 days before DeepSeek-V4.1-Flash, so benchmark comparisons should account for the intervening progress.
DeepSeek-V4.1-Flash is proprietary, while gpt-oss-20b is open weight.
On Humanity's Last Exam · no tools, DeepSeek-V4.1-Flash leads at 36.8% vs gpt-oss-20b at 10.9%. On Humanity's Last Exam · with tools, DeepSeek-V4.1-Flash leads at 63.9% vs gpt-oss-20b at 17.3%. On GPQA Diamond, DeepSeek-V4.1-Flash leads at 90.9% vs gpt-oss-20b at 71.5%.