gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimized to run on a single H100 GPU with native MXFP4 quantization. The model supports configurable reasoning depth, full chain-of-thought access, and native tool use, including function calling, browsing, and structured output generation.
Modalities
In / Out Price
$0.03 / $0.17per 1M
Context
131K
Released
Aug 5, 2025
Knowledge Cutoff
Jun 2024
Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).
The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.
Throughput is how fast the model writes (tokens per second — higher is better). Latency is total round-trip time (lower is better). TTFT is time-to-first-token — how long before you see anything appear (lower is better).
Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.
Scores on standardized evaluations. Higher percentages are better — and rank percentile shows where this model lands among all models on OpenRouter.
Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.
Token volume and request traffic to this model over time.
Drop-in code to call this model. OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. The only thing that changes between models is the model slug below.
gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimized to run on a single H100 GPU with native MXFP4 quantization.
gpt-oss-120b costs $0.03/M input tokens and $0.17/M output tokens, with separate rates for Cache Read at $0.03/M tokens.
gpt-oss-120b has a 131,072 token context window. It supports up to 131,072 completion tokens.
Yes. gpt-oss-120b accepts tools and tool_choice for function calling. It also supports structured outputs via a JSON schema in response_format.
gpt-oss-120b is served by 18 providers on OpenRouter: CoreWeave, DeepInfra (Turbo), AkashML, NovitaAI, SiliconFlow, DigitalOcean, Mancer, Google Vertex and 10 more. Requests are routed to the best available provider, with automatic failover to the others, and you can pin or exclude providers with provider routing.
gpt-oss-120b was released on August 5, 2025. Its knowledge cutoff is June 30, 2024.
| $0.03 | $0.17 | $0.03 | 0.39s | 34 tps | ||
| $0.037 | $0.17 | -- | 0.47s | 36 tps | ||
| $0.037 | $0.49 | -- | 0.83s | 112 tps | ||
| $0.05 | $0.25 | -- | 0.55s | 75 tps | ||
| $0.05 | $0.45 | -- | 1.45s | 26 tps | ||
| $0.055 | $0.385 | $0.02 | 0.40s | 66 tps | ||
| $0.085 | $0.50 | -- | 0.55s | 123 tps | ||
| $0.10 | $0.50 | $0.10 | 0.20s | 224 tps | ||
| $0.10 | $0.75 | $0.055 | 0.37s | 119 tps | ||
| $0.14 | $0.95 | -- | 0.83s | 403 tps | ||
| $0.15 | $0.60 | -- | 0.39s | 347 tps | ||
| $0.15 | $0.60 | -- | 0.45s | 99 tps | ||
| $0.15 | $0.60 | -- | 0.34s | 96 tps | ||
| $0.15 | $0.60 | -- | 0.27s | 297 tps | ||
| $0.15 | $0.60 | -- | -- | -- | ||
| $0.15 | $0.60 | -- | 0.76s | 80 tps | ||
| $0.15 | $0.60 | $0.075 | 0.23s | 366 tps | ||
| $0.15 | $0.75 | -- | 1.46s | 181 tps | ||
| $0.35 | $0.75 | $0.35 | 0.22s | 743 tps | ||
| $0.09 | $0.36 | -- | 1.33s | 43 tps |
Throughput
743tok/s
P50, best across providers
Latency
0.20s
P50, best provider
100.00%
99.66%
When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and customization options.