Kimi-K2-0905

About

What Kimi-K2-0905 is

1. Model Introduction Kimi K2-Instruct-0905 is the latest, most capable version of Kimi K2. It is a state-of-the-art mixture-of-experts (MoE) language model, featuring 32 billion activated parameters and a total of 1 trillion parameters. Key Features - Enhanced agentic coding intelligence: Kimi K2-Instruct-0905 demonstrates significant improvements in performance on public benchmarks and real-world coding agent tasks. - Improved frontend coding experience: Kimi K2-Instruct-0905 offers advancements in both the aesthetics and practicality of frontend programming. - Extended context length: Kimi K2-Instruct-0905’s context window has been increased from 128k to 256k tokens, providing better support for long-horizon tasks. 2. Model Summary 3. Evaluation Results All K2-Instruct-0905 numbers are reported as mean ± std over five independent, full-test-set runs. Before each run we prune the repository so that every Git object unreachable from the target commit disappears; this guarantees the agent sees only the code that would legitimately be available at that point in history. Except for Terminal-Bench (Terminus-2), every result was produced with our in-house evaluation harness. The harness is derived from SWE-agent, but we clamp the context windows of the Bash and Edit tools and rewrite the system prompt to match the task semantics. All baseline figures denoted with an asterisk () are excerpted directly from their official report or public leaderboard; the remaining metrics were evaluated by us under conditions identical to those used for K2-Instruct-0905. For SWE-Dev we go one step further: we overwrite the original repository files and delete any test file that exercises the functions the agent is expected to generate, eliminating any indirect hints about the desired implementation. 4. Deployment [!Note] You can access Kimi K2's API on https://platform.moonshot.ai , we provide OpenAI/Anthropic-compatible API for you. The Anthropic-compatible API maps temperature by realtemperature = requesttemperature 0.6 for better compatible with existing applications. Our model checkpoints are stored in the block-fp8 format, you can find it on Huggingface. Currently, Kimi-K2 is re

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How language models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys kimi-k2-0905 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (kimi-k2-0905 below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"kimi-k2-0905","messages":[{"role":"user","content":"Hello"}]}'

Details

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Licence

Commercial licence needed

Sources

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