gpt-oss-20b vs Qwen3.5-9B
gpt-oss-20b (2025) and Qwen3.5-9B (2026) are general-purpose language models from OpenAI and Alibaba. gpt-oss-20b ships a 131k-token context window, while Qwen3.5-9B ships a 262k-token context window. On Google-Proof Q&A, Qwen3.5-9B leads by 12.9 pts. On pricing, gpt-oss-20b costs $0.03/1M input tokens versus $0.10/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
gpt-oss-20b is ~233% cheaper at $0.03/1M; pay for Qwen3.5-9B only for long-context analysis.
Decision scorecard
Local evidence first| Signal | gpt-oss-20b | Qwen3.5-9B |
|---|---|---|
| Best for | tool-calling agents and provider-routed production | multimodal apps, tool-calling agents, and provider-routed production |
| Decision fit | RAG, Agents, and Long context | RAG, Agents, and Long context |
| Context window | 131k | 262k |
| Cheapest output | $0.14/1M tokens | $0.15/1M tokens |
| Provider routes | 9 tracked | 3 tracked |
| Shared benchmarks | 1 rows | Google-Proof Q&A leader |
Decision tradeoffs
- gpt-oss-20b has the lower cheapest tracked output price at $0.14/1M tokens.
- gpt-oss-20b has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags gpt-oss-20b for RAG, Agents, and Long context.
- Qwen3.5-9B leads the largest shared benchmark signal on Google-Proof Q&A by 12.9 points.
- Qwen3.5-9B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen3.5-9B uniquely exposes Vision and Multimodal in local model data.
- Local decision data tags Qwen3.5-9B for RAG, Agents, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
gpt-oss-20b
$59.00
Cheapest tracked route/tier: OpenRouter
Qwen3.5-9B
$118
Cheapest tracked route/tier: Together AI
Estimated monthly gap: $58.50. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- Qwen3.5-9B is $0.01/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Qwen3.5-9B adds Vision and Multimodal in local capability data.
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- gpt-oss-20b is $0.01/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision and Multimodal before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-08-05 | 2026-03-02 |
| Context window | 131k | 262k |
| Parameters | 20B | 9B |
| Architecture | decoder only | decoder only |
| License | Open Weights | Apache 2.0 |
| Knowledge cutoff | 2025-08 | - |
Pricing and availability
| Pricing attribute | gpt-oss-20b | Qwen3.5-9B |
|---|---|---|
| Input price | $0.03/1M tokens | $0.10/1M tokens |
| Output price | $0.14/1M tokens | $0.15/1M tokens |
| Providers |
Capabilities
| Capability | gpt-oss-20b | Qwen3.5-9B |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | gpt-oss-20b | Qwen3.5-9B |
|---|---|---|
| Google-Proof Q&A | 68.8 | 81.7 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has gpt-oss-20b at 68.8 and Qwen3.5-9B at 81.7, with Qwen3.5-9B ahead by 12.9 points. The largest visible gap is 12.9 points on Google-Proof Q&A, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on vision: Qwen3.5-9B and multimodal input: Qwen3.5-9B. Both models share function calling, tool use, and structured outputs, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
For cost, gpt-oss-20b lists $0.03/1M input and $0.14/1M output tokens on the cheapest tracked provider, while Qwen3.5-9B lists $0.10/1M input and $0.15/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts gpt-oss-20b lower by about $0.05 per million blended tokens. Availability is 9 providers versus 3, so concentration risk also matters.
Choose gpt-oss-20b when provider fit, lower input-token cost, and broader provider choice are central to the workload. Choose Qwen3.5-9B when long-context analysis and larger context windows are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, gpt-oss-20b or Qwen3.5-9B?
Qwen3.5-9B supports 262k tokens, while gpt-oss-20b supports 131k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is cheaper, gpt-oss-20b or Qwen3.5-9B?
gpt-oss-20b is cheaper on tracked token pricing. gpt-oss-20b costs $0.03/1M input and $0.14/1M output tokens. Qwen3.5-9B costs $0.10/1M input and $0.15/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is gpt-oss-20b or Qwen3.5-9B open source?
gpt-oss-20b is listed under Open Weights. Qwen3.5-9B is listed under Apache 2.0. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for vision, gpt-oss-20b or Qwen3.5-9B?
Qwen3.5-9B has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, gpt-oss-20b or Qwen3.5-9B?
Qwen3.5-9B has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run gpt-oss-20b and Qwen3.5-9B?
gpt-oss-20b is available on Cloudflare Workers AI, OpenRouter, Fireworks AI, GCP Vertex AI, and NVIDIA NIM. Qwen3.5-9B is available on Together AI, OpenRouter, and Alibaba Cloud PAI-EAS. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.