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Favicon for Cerebras

Cerebras

Browse models provided by Cerebras (Terms of Service)

3 models

Tokens processed on OpenRouter

  • OpenAI: gpt-oss-120bgpt-oss-120b

    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.

    by openai131K context$0.35/M input tokens$0.75/M output tokens
  • Qwen: Qwen3 235B A22B Instruct 2507Qwen3 235B A22B Instruct 2507

    Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass. It is optimized for general-purpose text generation, including instruction following, logical reasoning, math, code, and tool usage. The model supports a native 262K context length and does not implement "thinking mode" (<think> blocks). Compared to its base variant, this version delivers significant gains in knowledge coverage, long-context reasoning, coding benchmarks, and alignment with open-ended tasks. It is particularly strong on multilingual understanding, math reasoning (e.g., AIME, HMMT), and alignment evaluations like Arena-Hard and WritingBench.

    by qwen262K context$0.60/M input tokens$1.20/M output tokens
  • Meta: Llama 3.1 8B InstructLlama 3.1 8B Instruct

    Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 8B instruct-tuned version is fast and efficient. It has demonstrated strong performance compared to leading closed-source models in human evaluations. To read more about the model release, click here. Usage of this model is subject to Meta's Acceptable Use Policy.

    by meta-llama131K context$0.10/M input tokens$0.10/M output tokens