Model Comparison

Kimi K2 Instruct vs Qwen3 VL 4B Instruct

Kimi K2 Instruct significantly outperforms across most benchmarks. Qwen3 VL 4B Instruct is 2.2x cheaper per token.

Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

8 benchmarks

Kimi K2 Instruct outperforms in 7 benchmarks (AIME 2025, IFEval, LiveCodeBench v6, MMLU, MMLU-Pro, MMLU-Redux, SuperGPQA), while Qwen3 VL 4B Instruct is better at 1 benchmark (SimpleQA).

Kimi K2 Instruct significantly outperforms across most benchmarks.

Fri May 15 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Qwen3 VL 4B Instruct costs less

For input processing, Kimi K2 Instruct ($0.50/1M tokens) is 5.0x more expensive than Qwen3 VL 4B Instruct ($0.10/1M tokens).

For output processing, Kimi K2 Instruct ($0.50/1M tokens) is 1.2x cheaper than Qwen3 VL 4B Instruct ($0.60/1M tokens).

In conclusion, Kimi K2 Instruct is more expensive than Qwen3 VL 4B Instruct.*

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

Lowest available price from all providers

Fri May 15 2026 • llm-stats.com

Kimi K2 Instruct

Input tokens$0.50

Output tokens$0.50

Best providerFireworks

Qwen3 VL 4B Instruct

Input tokens$0.10

Output tokens$0.60

Best providerDeepinfra

Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

996.0B diff

Kimi K2 Instruct has 996.0B more parameters than Qwen3 VL 4B Instruct, making it 24900.0% larger.

Kimi K2 Instruct

1.0Tparameters

Qwen3 VL 4B Instruct

4.0Bparameters

1000.0B

Kimi K2 Instruct

4.0B

Qwen3 VL 4B Instruct

Context Window

Maximum input and output token capacity

Qwen3 VL 4B Instruct accepts 262,144 input tokens compared to Kimi K2 Instruct's 200,000 tokens. Qwen3 VL 4B Instruct can generate longer responses up to 262,144 tokens, while Kimi K2 Instruct is limited to 200,000 tokens.

Kimi K2 Instruct

Input200,000 tokens

Output200,000 tokens

Qwen3 VL 4B Instruct

Input262,144 tokens

Output262,144 tokens

Fri May 15 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen3 VL 4B Instruct supports multimodal inputs, whereas Kimi K2 Instruct does not.

Qwen3 VL 4B Instruct 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 4B Instruct

Text

Images

Audio

Video

License

Usage and distribution terms

Kimi K2 Instruct is licensed under MIT, while Qwen3 VL 4B Instruct 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 4B Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

Kimi K2 Instruct was released on 2025-07-11, while Qwen3 VL 4B Instruct was released on 2025-09-22.

Qwen3 VL 4B Instruct is 2 months newer than Kimi K2 Instruct.

Kimi K2 Instruct

Jul 11, 2025

10 months ago

Qwen3 VL 4B Instruct

Sep 22, 2025

7 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 4B Instruct is available from DeepInfra.

Kimi K2 Instruct

Fireworks

Input Price:Input: $0.50/1MOutput Price:Output: $0.50/1M

Novita

Input Price:Input: $0.57/1MOutput Price:Output: $2.30/1M

Qwen3 VL 4B Instruct

Deepinfra

Input Price:Input: $0.10/1MOutput Price:Output: $0.60/1M

* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Kimi K2 Instruct

View details

Moonshot AI

Less expensive output tokens

Higher AIME 2025 score (49.5% vs 46.6%)

Higher IFEval score (89.8% vs 82.3%)

Higher LiveCodeBench v6 score (53.7% vs 37.9%)

Higher MMLU score (89.5% vs 77.2%)

Higher MMLU-Pro score (81.1% vs 67.1%)

Higher MMLU-Redux score (92.7% vs 81.5%)

Higher SuperGPQA score (57.2% vs 40.3%)

Qwen3 VL 4B Instruct

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)

Supports multimodal inputs

Less expensive input tokens

Higher SimpleQA score (48.0% vs 31.0%)

Detailed Comparison

AI Model Comparison Table
Feature	Kimi K2 Instruct	Qwen3 VL 4B Instruct

FAQ

Common questions about Kimi K2 Instruct vs Qwen3 VL 4B Instruct.

Which is better, Kimi K2 Instruct or Qwen3 VL 4B Instruct?

Kimi K2 Instruct significantly outperforms across most benchmarks. Kimi K2 Instruct is made by Moonshot AI and Qwen3 VL 4B Instruct 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 4B Instruct 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 4B Instruct scores DocVQAtest: 95.3%, ScreenSpot: 94.0%, OCRBench: 88.1%, MMBench-V1.1: 85.1%, AI2D: 84.1%.

Is Kimi K2 Instruct cheaper than Qwen3 VL 4B Instruct?

Qwen3 VL 4B Instruct is 5.0x cheaper for input tokens. Kimi K2 Instruct costs $0.50/M input and $0.50/M output via fireworks. Qwen3 VL 4B Instruct costs $0.10/M input and $0.60/M output via deepinfra.

What are the context window sizes for Kimi K2 Instruct and Qwen3 VL 4B Instruct?

Kimi K2 Instruct supports 200K tokens and Qwen3 VL 4B Instruct 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 4B Instruct?

Key differences include context window (200K vs 262K), input pricing ($0.50 vs $0.10/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 4B Instruct?

Kimi K2 Instruct is developed by Moonshot AI and Qwen3 VL 4B Instruct is developed by Alibaba Cloud / Qwen Team.