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    Browse models provided by Targon (Terms of Service)

    7 models

    Tokens processed on OpenRouter

    • Qwen: Qwen3 Coder 480B A35BQwen3 Coder 480B A35B

      Qwen3-Coder-480B-A35B-Instruct is a Mixture-of-Experts (MoE) code generation model developed by the Qwen team. It is optimized for agentic coding tasks such as function calling, tool use, and long-context reasoning over repositories. The model features 480 billion total parameters, with 35 billion active per forward pass (8 out of 160 experts). Pricing for the Alibaba endpoints varies by context length. Once a request is greater than 128k input tokens, the higher pricing is used.

      by qwen1.05M context$1/M input tokens$2/M output tokens
  3. 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$0.12/M input tokens$0.59/M output tokens
  4. MoonshotAI: Kimi K2 0711Kimi K2 0711

    Kimi K2 Instruct is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. It is optimized for agentic capabilities, including advanced tool use, reasoning, and code synthesis. Kimi K2 excels across a broad range of benchmarks, particularly in coding (LiveCodeBench, SWE-bench), reasoning (ZebraLogic, GPQA), and tool-use (Tau2, AceBench) tasks. It supports long-context inference up to 128K tokens and is designed with a novel training stack that includes the MuonClip optimizer for stable large-scale MoE training.

    by moonshotai131K context$0.14/M input tokens$2.49/M output tokens
  5. DeepSeek: R1 0528R1 0528

    May 28th update to the original DeepSeek R1 Performance on par with OpenAI o1, but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inference pass. Fully open-source model.

    by deepseek164K context$0.50/M input tokens$2/M output tokens
  6. DeepSeek: DeepSeek V3 0324DeepSeek V3 0324

    DeepSeek V3, a 685B-parameter, mixture-of-experts model, is the latest iteration of the flagship chat model family from the DeepSeek team. It succeeds the DeepSeek V3 model and performs really well on a variety of tasks.

    by deepseek131K context$0.25/M input tokens$0.85/M output tokens
  7. DeepSeek: R1R1

    DeepSeek R1 is here: Performance on par with OpenAI o1, but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inference pass. Fully open-source model & technical report. MIT licensed: Distill & commercialize freely!

    by deepseek164K context$0.40/M input tokens$2/M output tokens
  8. DeepSeek: DeepSeek V3DeepSeek V3

    DeepSeek-V3 is the latest model from the DeepSeek team, building upon the instruction following and coding abilities of the previous versions. Pre-trained on nearly 15 trillion tokens, the reported evaluations reveal that the model outperforms other open-source models and rivals leading closed-source models. For model details, please visit the DeepSeek-V3 repo for more information, or see the launch announcement.

    by deepseek131K context$0.30/M input tokens$0.85/M output tokens