Claude Opus 4.8 vs gpt-oss-20b
At a Glance
| Compare | Claude Opus 4.8Anthropic |  gpt-oss-20bOpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #18 of 4670.1 score · 3/3 sources · complete | UnrankedNot in the 46-model eligible cohort |
| CostLower is better · Published-token output estimate | #42 of 44$0.604 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| EfficiencyHigher is better · MM Efficiency v1.5 | #35 of 3840.5 score · 3/3 sources · complete | UnrankedNot in the 38-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $5.00Anthropic ↗ · Sep 3, 2026 | $0.018Openrouter ↗ · Sep 23, 2026 |
| Output priceFrom · USD / 1M tokens | $25.00Anthropic ↗ · Sep 3, 2026 | $0.090Openrouter ↗ · Sep 23, 2026 |
| Context windowMaximum documented tokens | 1,000K | 131K |
| Model facts checked | Aug 29, 2026View model evidence → | Aug 28, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
| Benchmark | Claude Opus 4.8 | gpt-oss-20b |
|---|---|---|
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,452.66100% of row best · rating · claude-opus-4-8; 95% CI [1448.52490690, 1456.79817098]; votes 53446; rank 40 | 1,287.3289% of row best · rating · gpt-oss-20b; 95% CI [1280.92242279, 1293.71473349]; votes 10393; rank 246 |
| Overall ResultCounted from the protocol-matched rows above | 1 benchmark winNo overall winner | 0 benchmark winsNo overall winner |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Side-by-Side Facts
| Field | Claude Opus 4.8 | gpt-oss-20b |
|---|---|---|
| Developer | Anthropic | OpenAI |
| Family | Claude 4 8 | Gpt Oss |
| Model | Claude Opus 4.8 | gpt-oss-20b |
| Version | Claude Opus 4.8 | gpt-oss-20b |
| Lifecycle | active | active |
| Released | 2026-05-28 | Unknown |
| Knowledge cutoff | 2026-01-01 | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 1,000K | 131K |
| Total parameters | Unknown | 20.9B |
| Active parameters | Unknown | 3.6B |
| License | Unknown | apache-2.0 |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Anthropic (Standard), Deepinfra (Standard) | Deepinfra (Standard), Groq (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Serverless, Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning, tools |
Claude Opus 4.8 Capabilities
gpt-oss-20b Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
Claude Opus 4.8Anthropic | vs | GPT-6 AstraOpenAI | cross-developer peersimage, text |
Claude Fable 5.1Anthropic | vs | Claude Opus 4.8Anthropic | family variantsimage, text |
Claude Opus 4.8Anthropic | vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
Claude Opus 4.8Anthropic | vs |  DeepSeek-V4.1-FlashDeepSeek | cross-developer peersimage, text |
Claude Opus 4.8Anthropic | vs | Grok 4.6xAI | cross-developer peersimage, text |
Claude Opus 4.8Anthropic | vs |  Qwen3.8-MaxQwen | cross-developer peersimage, text |
Claude Opus 4.8Anthropic | vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text |
 MiniMax-M3MiniMax | vs | Claude Opus 4.8Anthropic | cross-developer peersimage, text |
Claude Opus 4.8Anthropic | vs |  GLM-5.3Z.ai | cross-developer peerstext |
Claude Opus 4.8Anthropic | vs |  Hy4 previewTencent | cross-developer peerstext |
Claude Opus 4.8Anthropic | vs | Seed 2.1 ProByteDance Seed | cross-developer peersimage, text |
Claude Opus 4.8Anthropic | vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Claude Opus 4.8 vs gpt-oss-20b FAQs
Is Claude Opus 4.8 or gpt-oss-20b better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Opus 4.8 and gpt-oss-20b, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Claude Opus 4.8 or gpt-oss-20b?+
Claude Opus 4.8 is $5.00 and gpt-oss-20b is $0.018 per million tokens, so gpt-oss-20b is cheaper on this metric. Claude Opus 4.8 is $25.00 and gpt-oss-20b is $0.090 per million tokens, so gpt-oss-20b is cheaper on this metric.
Which has a larger context window, Claude Opus 4.8 or gpt-oss-20b?+
Claude Opus 4.8 has the larger sourced context window. Claude Opus 4.8 supports 1,000K and gpt-oss-20b supports 131K.
Which performs better in benchmarks, Claude Opus 4.8 or gpt-oss-20b?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Claude Opus 4.8 or gpt-oss-20b be self-hosted?+
gpt-oss-20b is the only model in this pair currently marked as self-hostable. Claude Opus 4.8 is not marked open weight; gpt-oss-20b is open weight.
Can Claude Opus 4.8 and gpt-oss-20b understand images?+
Claude Opus 4.8 is documented with image input; gpt-oss-20b is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Claude Opus 4.8 or gpt-oss-20b?+
Neither has a larger sourced maximum output. Claude Opus 4.8 is 128K and gpt-oss-20b is —.
Do Claude Opus 4.8 and gpt-oss-20b support reasoning and tool use?+
Claude Opus 4.8: reasoning, tool calling, and image input. gpt-oss-20b: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Claude Opus 4.8 or gpt-oss-20b?+
Claude Opus 4.8 has 2 sourced provider routes; gpt-oss-20b has 5, so gpt-oss-20b has broader tracked availability.
Which offers better value, Claude Opus 4.8 or gpt-oss-20b?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.