Model Comparison

Kimi K2 Instruct vs Kimi K2-Instruct-0905Which is better in 2026?

Kimi K2 Instruct has a slight edge in benchmark performance.

Verdict: Kimi K2 Instruct vs Kimi K2-Instruct-0905 — which is better?

Kimi K2 Instruct (by Moonshot AI) and Kimi K2-Instruct-0905 (by Moonshot AI) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

Kimi K2 Instruct outperforms in 1 benchmarks (Terminal-Bench), while Kimi K2-Instruct-0905 is better at 0 benchmarks. Kimi K2 Instruct has a slight edge in benchmark performance.

Choose Kimi K2 Instruct if…

  • you want predictable pricing at $0.50/M input and $0.50/M output

Choose Kimi K2-Instruct-0905 if…

  • you want the strongest raw capability — it leads on 26 of 27 shared benchmarks
  • you want the most recent training data — it shipped Sep 2025

Performance Benchmarks

Comparative analysis across standard metrics

27 benchmarks

Kimi K2 Instruct outperforms in 1 benchmarks (Terminal-Bench), while Kimi K2-Instruct-0905 is better at 0 benchmarks.

Kimi K2 Instruct has a slight edge in benchmark performance.

Tue Jul 21 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

0.0M diff

Kimi K2-Instruct-0905 has 0.0B more parameters than Kimi K2 Instruct, making it 0.0% larger.

Moonshot AI
Kimi K2 Instruct
1.0Tparameters
Moonshot AI
Kimi K2-Instruct-0905
1.0Tparameters
1000.0B
Kimi K2 Instruct
1000.0B
Kimi K2-Instruct-0905

Context Window

Maximum input and output token capacity

Only Kimi K2 Instruct specifies input context (200,000 tokens). Only Kimi K2 Instruct specifies output context (200,000 tokens).

Moonshot AI
Kimi K2 Instruct
Input200,000 tokens
Output200,000 tokens
Moonshot AI
Kimi K2-Instruct-0905
Input- tokens
Output- tokens
Tue Jul 21 2026 • llm-stats.com

License

Usage and distribution terms

Both models are licensed under MIT.

Both models share the same licensing terms, providing consistent usage rights.

Kimi K2 Instruct

MIT

Open weights

Kimi K2-Instruct-0905

MIT

Open weights

Release Timeline

When each model was launched

Kimi K2 Instruct was released on 2025-07-11, while Kimi K2-Instruct-0905 was released on 2025-09-05.

Kimi K2-Instruct-0905 is 2 months newer than Kimi K2 Instruct.

Kimi K2 Instruct

Jul 11, 2025

1.0 years ago

Kimi K2-Instruct-0905

Sep 5, 2025

10 months ago

1mo newer

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Moonshot AI
Larger context window (200,000 tokens)
Higher Terminal-Bench score (30.0% vs 25.0%)
Moonshot AI

No standout differentiators in the data we have for this pair.

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against Kimi K2 Instruct and Kimi K2-Instruct-0905 side-by-side, then vote on the output you prefer.

Kimi K2 Instruct
✓ Preferred
Kimi K2-Instruct-0905
Open in Playground
AI Model Comparison Table
Feature
Moonshot AI
Kimi K2 Instruct
Moonshot AI
Kimi K2-Instruct-0905

FAQ

Common questions about Kimi K2 Instruct vs Kimi K2-Instruct-0905.

Which is better, Kimi K2 Instruct or Kimi K2-Instruct-0905?

Kimi K2 Instruct has a slight edge in benchmark performance. Kimi K2 Instruct is made by Moonshot AI and Kimi K2-Instruct-0905 is made by Moonshot AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does Kimi K2 Instruct compare to Kimi K2-Instruct-0905 in benchmarks?

Kimi K2 Instruct scores MATH-500: 97.4%, GSM8k: 97.3%, CBNSL: 95.6%, HumanEval: 93.3%, MMLU-Redux: 92.7%. Kimi K2-Instruct-0905 scores MATH-500: 97.4%, MMLU-Redux: 92.7%, IFEval: 89.8%, AutoLogi: 89.5%, MMLU: 89.5%.

What are the context window sizes for Kimi K2 Instruct and Kimi K2-Instruct-0905?

Kimi K2 Instruct supports 200K tokens and Kimi K2-Instruct-0905 supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

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