Model Comparison

Kimi K2 Instruct vs Qwen3 VL 235B A22B ThinkingWhich is better in 2026?

Qwen3 VL 235B A22B Thinking significantly outperforms across most benchmarks. Kimi K2 Instruct is 2.4x cheaper per token.

Verdict: Kimi K2 Instruct vs Qwen3 VL 235B A22B Thinking — which is better?

Kimi K2 Instruct (by Moonshot AI) and Qwen3 VL 235B A22B Thinking (by Alibaba Cloud / Qwen Team) 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 (IFEval), while Qwen3 VL 235B A22B Thinking is better at 9 benchmarks (AIME 2025, Humanity's Last Exam, LiveCodeBench v6, MMLU, MMLU-Pro, MMLU-Redux, SimpleQA, SuperGPQA, ZebraLogic). Qwen3 VL 235B A22B Thinking significantly outperforms across most benchmarks.

On price, Kimi K2 Instruct is roughly 2.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Qwen3 VL 235B A22B Thinking also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.

Choose Kimi K2 Instruct if…

  • cost matters — it's about 2.4x cheaper per token

Choose Qwen3 VL 235B A22B Thinking if…

  • you want the strongest raw capability — it leads on 9 of 10 shared benchmarks
  • you process long inputs — it offers a 262,144 token context window
  • you want the most recent training data — it shipped Sep 2025
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Performance Benchmarks

Comparative analysis across standard metrics

10 benchmarks

Kimi K2 Instruct outperforms in 1 benchmarks (IFEval), while Qwen3 VL 235B A22B Thinking is better at 9 benchmarks (AIME 2025, Humanity's Last Exam, LiveCodeBench v6, MMLU, MMLU-Pro, MMLU-Redux, SimpleQA, SuperGPQA, ZebraLogic).

Qwen3 VL 235B A22B Thinking significantly outperforms across most benchmarks.

Tue Jun 16 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Kimi K2 Instruct costs less

For input processing, Kimi K2 Instruct ($0.50/1M tokens) is 1.1x more expensive than Qwen3 VL 235B A22B Thinking ($0.45/1M tokens).

For output processing, Kimi K2 Instruct ($0.50/1M tokens) is 7.0x cheaper than Qwen3 VL 235B A22B Thinking ($3.49/1M tokens).

In conclusion, Qwen3 VL 235B A22B Thinking is more expensive than Kimi K2 Instruct.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Tue Jun 16 2026 • llm-stats.com
Moonshot AI
Kimi K2 Instruct
Input tokens$0.50
Output tokens$0.50
Best providerFireworks
Alibaba Cloud / Qwen Team
Qwen3 VL 235B A22B Thinking
Input tokens$0.45
Output tokens$3.49
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

764.0B diff

Kimi K2 Instruct has 764.0B more parameters than Qwen3 VL 235B A22B Thinking, making it 323.7% larger.

Moonshot AI
Kimi K2 Instruct
1.0Tparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 235B A22B Thinking
236.0Bparameters
1000.0B
Kimi K2 Instruct
236.0B
Qwen3 VL 235B A22B Thinking

Context Window

Maximum input and output token capacity

Qwen3 VL 235B A22B Thinking accepts 262,144 input tokens compared to Kimi K2 Instruct's 200,000 tokens. Qwen3 VL 235B A22B Thinking can generate longer responses up to 262,144 tokens, while Kimi K2 Instruct is limited to 200,000 tokens.

Moonshot AI
Kimi K2 Instruct
Input200,000 tokens
Output200,000 tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 235B A22B Thinking
Input262,144 tokens
Output262,144 tokens
Tue Jun 16 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen3 VL 235B A22B Thinking supports multimodal inputs, whereas Kimi K2 Instruct does not.

Qwen3 VL 235B A22B Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.

Kimi K2 Instruct

Text
Images
Audio
Video

Qwen3 VL 235B A22B Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

Kimi K2 Instruct is licensed under MIT, while Qwen3 VL 235B A22B Thinking uses Apache 2.0.

License differences may affect how you can use these models in commercial or open-source projects.

Kimi K2 Instruct

MIT

Open weights

Qwen3 VL 235B A22B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

Kimi K2 Instruct was released on 2025-07-11, while Qwen3 VL 235B A22B Thinking was released on 2025-09-22.

Qwen3 VL 235B A22B Thinking is 2 months newer than Kimi K2 Instruct.

Kimi K2 Instruct

Jul 11, 2025

11 months ago

Qwen3 VL 235B A22B Thinking

Sep 22, 2025

8 months ago

2mo 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

Provider Availability

Kimi K2 Instruct is available from Fireworks, Novita. Qwen3 VL 235B A22B Thinking is available from DeepInfra, Novita.

Kimi K2 Instruct

fireworks logo
Fireworks
Input Price:Input: $0.50/1MOutput Price:Output: $0.50/1M
novita logo
Novita
Input Price:Input: $0.57/1MOutput Price:Output: $2.30/1M

Qwen3 VL 235B A22B Thinking

deepinfra logo
Deepinfra
Input Price:Input: $0.45/1MOutput Price:Output: $3.49/1M
novita logo
Novita
Input Price:Input: $0.98/1MOutput Price:Output: $3.95/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Moonshot AI
Less expensive output tokens
Higher IFEval score (89.8% vs 88.2%)
Alibaba Cloud / Qwen Team
Larger context window (262,144 tokens)
Supports multimodal inputs
Less expensive input tokens
Higher AIME 2025 score (89.7% vs 49.5%)
Higher Humanity's Last Exam score (13.6% vs 4.7%)
Higher LiveCodeBench v6 score (70.1% vs 53.7%)
Higher MMLU score (90.6% vs 89.5%)
Higher MMLU-Pro score (83.8% vs 81.1%)
Higher MMLU-Redux score (93.7% vs 92.7%)
Higher SimpleQA score (44.4% vs 31.0%)
Higher SuperGPQA score (64.3% vs 57.2%)
Higher ZebraLogic score (97.3% vs 89.0%)

Detailed Comparison

AI Model Comparison Table
Feature
Moonshot AI
Kimi K2 Instruct
Alibaba Cloud / Qwen Team
Qwen3 VL 235B A22B Thinking

FAQ

Common questions about Kimi K2 Instruct vs Qwen3 VL 235B A22B Thinking.

Which is better, Kimi K2 Instruct or Qwen3 VL 235B A22B Thinking?

Qwen3 VL 235B A22B Thinking significantly outperforms across most benchmarks. Kimi K2 Instruct is made by Moonshot AI and Qwen3 VL 235B A22B Thinking is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does Kimi K2 Instruct compare to Qwen3 VL 235B A22B Thinking in benchmarks?

Kimi K2 Instruct scores MATH-500: 97.4%, GSM8k: 97.3%, CBNSL: 95.6%, HumanEval: 93.3%, MMLU-Redux: 92.7%. Qwen3 VL 235B A22B Thinking scores ZebraLogic: 97.3%, DocVQAtest: 96.5%, ScreenSpot: 95.4%, CountBench: 93.7%, MMLU-Redux: 93.7%.

Is Kimi K2 Instruct cheaper than Qwen3 VL 235B A22B Thinking?

Qwen3 VL 235B A22B Thinking is 1.1x cheaper for input tokens. Kimi K2 Instruct costs $0.50/M input and $0.50/M output via fireworks. Qwen3 VL 235B A22B Thinking costs $0.45/M input and $3.49/M output via deepinfra.

What are the context window sizes for Kimi K2 Instruct and Qwen3 VL 235B A22B Thinking?

Kimi K2 Instruct supports 200K tokens and Qwen3 VL 235B A22B Thinking supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Kimi K2 Instruct and Qwen3 VL 235B A22B Thinking?

Key differences include context window (200K vs 262K), input pricing ($0.50 vs $0.45/M), multimodal support (no vs yes), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes Kimi K2 Instruct and Qwen3 VL 235B A22B Thinking?

Kimi K2 Instruct is developed by Moonshot AI and Qwen3 VL 235B A22B Thinking is developed by Alibaba Cloud / Qwen Team.

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