Model Comparison

DeepSeek-V3.1 vs Kimi K2 Instruct

DeepSeek-V3.1 shows notably better performance in the majority of benchmarks. DeepSeek-V3.1 is 1.1x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

11 benchmarks

DeepSeek-V3.1 outperforms in 7 benchmarks (Aider-Polyglot, AIME 2025, Humanity's Last Exam, MMLU-Pro, SimpleQA, SWE-bench Multilingual, Terminal-Bench), while Kimi K2 Instruct is better at 4 benchmarks (AIME 2024, GPQA, HMMT 2025, MMLU-Redux).

DeepSeek-V3.1 shows notably better performance in the majority of benchmarks.

Sat May 30 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

DeepSeek-V3.1 costs less

For input processing, DeepSeek-V3.1 ($0.27/1M tokens) is 1.9x cheaper than Kimi K2 Instruct ($0.50/1M tokens).

For output processing, DeepSeek-V3.1 ($1.00/1M tokens) is 2.0x more expensive than Kimi K2 Instruct ($0.50/1M tokens).

In conclusion, Kimi K2 Instruct is more expensive than DeepSeek-V3.1.*

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

Lowest available price from all providers
Sat May 30 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.1
Input tokens$0.27
Output tokens$1.00
Best providerDeepinfra
Moonshot AI
Kimi K2 Instruct
Input tokens$0.50
Output tokens$0.50
Best providerFireworks
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

329.0B diff

Kimi K2 Instruct has 329.0B more parameters than DeepSeek-V3.1, making it 49.0% larger.

DeepSeek
DeepSeek-V3.1
671.0Bparameters
Moonshot AI
Kimi K2 Instruct
1.0Tparameters
671.0B
DeepSeek-V3.1
1000.0B
Kimi K2 Instruct

Context Window

Maximum input and output token capacity

Kimi K2 Instruct accepts 200,000 input tokens compared to DeepSeek-V3.1's 163,840 tokens. Kimi K2 Instruct can generate longer responses up to 200,000 tokens, while DeepSeek-V3.1 is limited to 163,840 tokens.

DeepSeek
DeepSeek-V3.1
Input163,840 tokens
Output163,840 tokens
Moonshot AI
Kimi K2 Instruct
Input200,000 tokens
Output200,000 tokens
Sat May 30 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.

DeepSeek-V3.1

MIT

Open weights

Kimi K2 Instruct

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.1 was released on 2025-01-10, while Kimi K2 Instruct was released on 2025-07-11.

Kimi K2 Instruct is 6 months newer than DeepSeek-V3.1.

DeepSeek-V3.1

Jan 10, 2025

1.4 years ago

Kimi K2 Instruct

Jul 11, 2025

10 months ago

6mo 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

DeepSeek-V3.1 is available from DeepInfra, Novita. Kimi K2 Instruct is available from Fireworks, Novita.

DeepSeek-V3.1

deepinfra logo
Deepinfra
Input Price:Input: $0.27/1MOutput Price:Output: $1.00/1M
novita logo
Novita
Input Price:Input: $0.27/1MOutput Price:Output: $1.00/1M

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
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek
Less expensive input tokens
Higher Aider-Polyglot score (68.4% vs 60.0%)
Higher AIME 2025 score (49.8% vs 49.5%)
Higher Humanity's Last Exam score (15.9% vs 4.7%)
Higher MMLU-Pro score (83.7% vs 81.1%)
Higher SimpleQA score (93.4% vs 31.0%)
Higher SWE-bench Multilingual score (54.5% vs 47.3%)
Higher Terminal-Bench score (31.3% vs 30.0%)
Moonshot AI
Larger context window (200,000 tokens)
Less expensive output tokens
Higher AIME 2024 score (69.6% vs 66.3%)
Higher GPQA score (75.1% vs 74.9%)
Higher HMMT 2025 score (38.8% vs 33.5%)
Higher MMLU-Redux score (92.7% vs 91.8%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.1
Moonshot AI
Kimi K2 Instruct

FAQ

Common questions about DeepSeek-V3.1 vs Kimi K2 Instruct.

Which is better, DeepSeek-V3.1 or Kimi K2 Instruct?

DeepSeek-V3.1 shows notably better performance in the majority of benchmarks. DeepSeek-V3.1 is made by DeepSeek and Kimi K2 Instruct is made by Moonshot AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek-V3.1 compare to Kimi K2 Instruct in benchmarks?

DeepSeek-V3.1 scores SimpleQA: 93.4%, MMLU-Redux: 91.8%, MMLU-Pro: 83.7%, GPQA: 74.9%, CodeForces: 69.7%. Kimi K2 Instruct scores MATH-500: 97.4%, GSM8k: 97.3%, CBNSL: 95.6%, HumanEval: 93.3%, MMLU-Redux: 92.7%.

Is DeepSeek-V3.1 cheaper than Kimi K2 Instruct?

DeepSeek-V3.1 is 1.9x cheaper for input tokens. DeepSeek-V3.1 costs $0.27/M input and $1.00/M output via deepinfra. Kimi K2 Instruct costs $0.50/M input and $0.50/M output via fireworks.

What are the context window sizes for DeepSeek-V3.1 and Kimi K2 Instruct?

DeepSeek-V3.1 supports 164K tokens and Kimi K2 Instruct supports 200K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V3.1 and Kimi K2 Instruct?

Key differences include context window (164K vs 200K), input pricing ($0.27 vs $0.50/M). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3.1 and Kimi K2 Instruct?

DeepSeek-V3.1 is developed by DeepSeek and Kimi K2 Instruct is developed by Moonshot AI.