Qwen3.8 27B vs gpt-oss-120b
At a Glance
| Compare |  Qwen3.8 27BQwen |  gpt-oss-120bOpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #30 of 4654.0 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 36.0–69.4 | UnrankedNot in the 46-model eligible cohort |
| CostLower is better · Published-token output estimate | #16 of 44$0.072 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| EfficiencyHigher is better · MM Efficiency v1.5 | #17 of 3854.8 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 45.8–62.5 | UnrankedNot in the 38-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.20Deepinfra ↗ · Sep 21, 2026 | $0.037Deepinfra ↗ · Sep 21, 2026 |
| Output priceFrom · USD / 1M tokens | $2.50Deepinfra ↗ · Sep 21, 2026 | $0.17Deepinfra ↗ · Sep 21, 2026 |
| Context windowMaximum documented tokens | 262K | 131K |
| Model facts checked | Aug 28, 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 | Qwen3.8-27B | gpt-oss-120b |
|---|---|---|
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,439.27100% of row best · rating · qwen3.8-27b; 95% CI [1432.84410050, 1445.69542028]; votes 10697; rank 68 | 1,365.8795% of row best · rating · gpt-oss-120b; 95% CI [1361.49487468, 1370.23719284]; votes 29955; rank 176 |
| 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 | Qwen3.8-27B | gpt-oss-120b |
|---|---|---|
| Developer | Qwen | OpenAI |
| Family | Qwen3 8 27b | Gpt Oss 120b |
| Model | Qwen3.8-27B | gpt-oss-120b |
| Version | Qwen3.8-27B | gpt-oss-120b |
| Lifecycle | active | active |
| Released | Unknown | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 262K | 131K |
| Total parameters | 27.8B | 116.8B |
| Active parameters | Unknown | 5.1B |
| License | apache-2.0 | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard) | Cerebras (Standard), Deepinfra (Standard), Fireworks Ai (Serverless, Standard), Groq (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Serverless, Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning, tools |
Qwen3.8 27B Capabilities
gpt-oss-120b Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
Qwen3.8-27BQwen | vs | GPT-6 AstraOpenAI | cross-developer peersimage, text |
Qwen3.8-27BQwen | vs | Claude Fable 5.1Anthropic | cross-developer peersimage, text |
Qwen3.8-27BQwen | vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
Qwen3.8-27BQwen | vs |  DeepSeek-V4.1-FlashDeepSeek | cross-developer peersimage, text |
Qwen3.8-27BQwen | vs | Grok 4.6xAI | cross-developer peersimage, text |
Qwen3.8-27BQwen | vs | Qwen3.8-MaxQwen | family variantsimage, text |
Qwen3.8-27BQwen | vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text |
 MiniMax-M3MiniMax | vs | Qwen3.8-27BQwen | cross-developer peersimage, text |
Qwen3.8-27BQwen | vs |  GLM-5.3Z.ai | cross-developer peerstext |
Qwen3.8-27BQwen | vs |  Hy4 previewTencent | cross-developer peerstext |
Qwen3.8-27BQwen | vs | Seed 2.1 ProByteDance Seed | cross-developer peersimage, text |
Qwen3.8-27BQwen | vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Qwen3.8 27B vs gpt-oss-120b FAQs
Is Qwen3.8 27B or gpt-oss-120b better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.8 27B and gpt-oss-120b, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Qwen3.8 27B or gpt-oss-120b?+
Qwen3.8 27B is $0.20 and gpt-oss-120b is $0.037 per million tokens, so gpt-oss-120b is cheaper on this metric. Qwen3.8 27B is $2.50 and gpt-oss-120b is $0.17 per million tokens, so gpt-oss-120b is cheaper on this metric.
Which has a larger context window, Qwen3.8 27B or gpt-oss-120b?+
Qwen3.8 27B has the larger sourced context window. Qwen3.8 27B supports 262K and gpt-oss-120b supports 131K.
Which performs better in benchmarks, Qwen3.8 27B or gpt-oss-120b?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Qwen3.8 27B or gpt-oss-120b be self-hosted?+
Both models have the same recorded self-hosting status: supported. Qwen3.8 27B is open weight; gpt-oss-120b is open weight.
Can Qwen3.8 27B and gpt-oss-120b understand images?+
Qwen3.8 27B is documented with image input; gpt-oss-120b is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Qwen3.8 27B or gpt-oss-120b?+
Neither has a larger sourced maximum output. Qwen3.8 27B is 131K and gpt-oss-120b is —.
Do Qwen3.8 27B and gpt-oss-120b support reasoning and tool use?+
Qwen3.8 27B: reasoning, tool calling, and image input. gpt-oss-120b: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Qwen3.8 27B or gpt-oss-120b?+
Qwen3.8 27B has 3 sourced provider routes; gpt-oss-120b has 7, so gpt-oss-120b has broader tracked availability.
Which offers better value, Qwen3.8 27B or gpt-oss-120b?+
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.