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Model Comparison

Kimi K2.5 vs DeepSeek-V4-Pro-Max

DeepSeek-V4-Pro-Max significantly outperforms across most benchmarks. Kimi K2.5 is 1.8x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

9 benchmarks

Kimi K2.5 outperforms in 1 benchmarks (Humanity's Last Exam), while DeepSeek-V4-Pro-Max is better at 8 benchmarks (BrowseComp, GPQA, IMO-AnswerBench, MMLU-Pro, SWE-bench Multilingual, SWE-Bench Pro, SWE-Bench Verified, Terminal-Bench 2.0).

DeepSeek-V4-Pro-Max significantly outperforms across most benchmarks.

Fri Apr 24 2026 • llm-stats.com

Arena Performance

Human preference votes

CallingBox

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One API for outbound and inbound calls.

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$0.05 /min all-in7 lines of code

Pricing Analysis

Price comparison per million tokens

Kimi K2.5 costs less

For input processing, Kimi K2.5 ($0.60/1M tokens) is 2.9x cheaper than DeepSeek-V4-Pro-Max ($1.74/1M tokens).

For output processing, Kimi K2.5 ($3.00/1M tokens) is 1.2x cheaper than DeepSeek-V4-Pro-Max ($3.48/1M tokens).

In conclusion, DeepSeek-V4-Pro-Max is more expensive than Kimi K2.5.*

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

Lowest available price from all providers
Fri Apr 24 2026 • llm-stats.com
Moonshot AI
Kimi K2.5
Input tokens$0.60
Output tokens$3.00
Best providerFireworks
DeepSeek
DeepSeek-V4-Pro-Max
Input tokens$1.74
Output tokens$3.48
Best providerDeepSeek
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

600.0B diff

DeepSeek-V4-Pro-Max has 600.0B more parameters than Kimi K2.5, making it 60.0% larger.

Moonshot AI
Kimi K2.5
1000.0Bparameters
DeepSeek
DeepSeek-V4-Pro-Max
1600.0Bparameters
1000.0B
Kimi K2.5
1600.0B
DeepSeek-V4-Pro-Max

Context Window

Maximum input and output token capacity

DeepSeek-V4-Pro-Max accepts 1,048,576 input tokens compared to Kimi K2.5's 262,100 tokens. DeepSeek-V4-Pro-Max can generate longer responses up to 393,216 tokens, while Kimi K2.5 is limited to 262,100 tokens.

Moonshot AI
Kimi K2.5
Input262,100 tokens
Output262,100 tokens
DeepSeek
DeepSeek-V4-Pro-Max
Input1,048,576 tokens
Output393,216 tokens
Fri Apr 24 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Kimi K2.5 supports multimodal inputs, whereas DeepSeek-V4-Pro-Max does not.

Kimi K2.5 can handle both text and other forms of data like images, making it suitable for multimodal applications.

Kimi K2.5

Text
Images
Audio
Video

DeepSeek-V4-Pro-Max

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under MIT.

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

Kimi K2.5

MIT

Open weights

DeepSeek-V4-Pro-Max

MIT

Open weights

Release Timeline

When each model was launched

Kimi K2.5 was released on 2026-01-27, while DeepSeek-V4-Pro-Max was released on 2026-04-23.

DeepSeek-V4-Pro-Max is 3 months newer than Kimi K2.5.

Kimi K2.5

Jan 27, 2026

2 months ago

DeepSeek-V4-Pro-Max

Apr 23, 2026

1 days 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 K2.5 is available from Fireworks, Moonshot AI. DeepSeek-V4-Pro-Max is available from DeepSeek.

Kimi K2.5

fireworks logo
Fireworks
Input Price:Input: $0.60/1MOutput Price:Output: $3.00/1M
moonshot logo
Unknown Organization
Input Price:Input: $0.60/1MOutput Price:Output: $3.00/1M

DeepSeek-V4-Pro-Max

deepseek logo
DeepSeek
Input Price:Input: $1.74/1MOutput Price:Output: $3.48/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Moonshot AI
Supports multimodal inputs
Less expensive input tokens
Less expensive output tokens
Higher Humanity's Last Exam score (50.2% vs 48.2%)
DeepSeek
Larger context window (1,048,576 tokens)
Higher BrowseComp score (83.4% vs 74.9%)
Higher GPQA score (90.1% vs 87.6%)
Higher IMO-AnswerBench score (89.8% vs 81.8%)
Higher MMLU-Pro score (87.5% vs 87.1%)
Higher SWE-bench Multilingual score (76.2% vs 73.0%)
Higher SWE-Bench Pro score (55.4% vs 50.7%)
Higher SWE-Bench Verified score (80.6% vs 76.8%)
Higher Terminal-Bench 2.0 score (67.9% vs 50.8%)

Detailed Comparison

AI Model Comparison Table
Feature
Moonshot AI
Kimi K2.5
DeepSeek
DeepSeek-V4-Pro-Max

FAQ

Common questions about Kimi K2.5 vs DeepSeek-V4-Pro-Max

DeepSeek-V4-Pro-Max significantly outperforms across most benchmarks. Kimi K2.5 is made by Moonshot AI and DeepSeek-V4-Pro-Max is made by DeepSeek. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
Kimi K2.5 scores AIME 2025: 96.1%, HMMT 2025: 95.4%, InfoVQAtest: 92.6%, OCRBench: 92.3%, MathVista-Mini: 90.1%. DeepSeek-V4-Pro-Max scores CodeForces: 100.0%, HMMT Feb 26: 95.2%, LiveCodeBench: 93.5%, MathArena Apex: 90.2%, GPQA: 90.1%.
Kimi K2.5 is 2.9x cheaper for input tokens. Kimi K2.5 costs $0.60/M input and $3.00/M output via fireworks. DeepSeek-V4-Pro-Max costs $1.74/M input and $3.48/M output via deepseek.
Kimi K2.5 supports 262K tokens and DeepSeek-V4-Pro-Max supports 1.0M tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (262K vs 1.0M), input pricing ($0.60 vs $1.74/M), multimodal support (yes vs no). See the full comparison above for benchmark-by-benchmark results.
Kimi K2.5 is developed by Moonshot AI and DeepSeek-V4-Pro-Max is developed by DeepSeek.