gpt-oss-120b vs Kimi K2 Thinking Turbo
gpt-oss-120b (2025) and Kimi K2 Thinking Turbo (2025) are general-purpose language models from OpenAI and Moonshot AI. gpt-oss-120b ships a 131K-token context window, while Kimi K2 Thinking Turbo ships a 262K-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing. The goal is to make the tradeoff clear before deeper testing.
Kimi K2 Thinking Turbo is safer overall; choose gpt-oss-120b when provider fit matters.
Decision scorecard
Local evidence first| Signal | gpt-oss-120b | Kimi K2 Thinking Turbo |
|---|---|---|
| Decision fit | RAG, Agents, and Long context | Long context |
| Context window | 131K | 262K |
| Cheapest output | $0.18/1M tokens | - |
| Provider routes | 7 tracked | 0 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- gpt-oss-120b has broader tracked provider coverage for fallback and procurement flexibility.
- gpt-oss-120b uniquely exposes Function calling, Tool use, and Structured outputs in local model data.
- Local decision data tags gpt-oss-120b for RAG, Agents, and Long context.
- Kimi K2 Thinking Turbo has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Kimi K2 Thinking Turbo for Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
gpt-oss-120b
$76.20
Cheapest tracked route: OpenRouter
Kimi K2 Thinking Turbo
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for gpt-oss-120b and Kimi K2 Thinking Turbo; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Function calling, Tool use, and Structured outputs before moving production traffic.
- No overlapping tracked provider route is sourced for Kimi K2 Thinking Turbo and gpt-oss-120b; plan for SDK, billing, or endpoint changes.
- gpt-oss-120b adds Function calling, Tool use, and Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-08-05 | 2025-11-06 |
| Context window | 131K | 262K |
| Parameters | 120B | — |
| Architecture | decoder only | - |
| License | Open Source | Proprietary |
| Knowledge cutoff | 2025-08 | - |
Pricing and availability
| Pricing attribute | gpt-oss-120b | Kimi K2 Thinking Turbo |
|---|---|---|
| Input price | $0.04/1M tokens | - |
| Output price | $0.18/1M tokens | - |
| Providers | - |
Capabilities
| Capability | gpt-oss-120b | Kimi K2 Thinking Turbo |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | No |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on function calling: gpt-oss-120b, tool use: gpt-oss-120b, and structured outputs: gpt-oss-120b. Both models share the core language-model surface, 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.
Pricing coverage is uneven: gpt-oss-120b has $0.04/1M input tokens and Kimi K2 Thinking Turbo has no token price sourced yet. Provider availability is 7 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose gpt-oss-120b when provider fit and broader provider choice are central to the workload. Choose Kimi K2 Thinking Turbo 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. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which has a larger context window, gpt-oss-120b or Kimi K2 Thinking Turbo?
Kimi K2 Thinking Turbo supports 262K tokens, while gpt-oss-120b supports 131K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is gpt-oss-120b or Kimi K2 Thinking Turbo open source?
gpt-oss-120b is listed under Open Source. Kimi K2 Thinking Turbo is listed under Proprietary. 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 function calling, gpt-oss-120b or Kimi K2 Thinking Turbo?
gpt-oss-120b has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for tool use, gpt-oss-120b or Kimi K2 Thinking Turbo?
gpt-oss-120b has the clearer documented tool use signal in this comparison. If tool use is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for structured outputs, gpt-oss-120b or Kimi K2 Thinking Turbo?
gpt-oss-120b has the clearer documented structured outputs signal in this comparison. If structured outputs 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-120b and Kimi K2 Thinking Turbo?
gpt-oss-120b is available on OpenRouter, Together AI, Fireworks AI, GCP Vertex AI, and NVIDIA NIM. Kimi K2 Thinking Turbo is available on the tracked providers still being sourced. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.