Model Comparison

DeepSeek-V4-Flash-Max vs Kimi K2.6Which is better in 2026?

Kimi K2.6 shows notably better performance in the majority of benchmarks. DeepSeek-V4-Flash-Max is 11.5x cheaper per token.

Verdict: DeepSeek-V4-Flash-Max vs Kimi K2.6 — which is better?

DeepSeek-V4-Flash-Max (by DeepSeek) and Kimi K2.6 (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.

DeepSeek-V4-Flash-Max outperforms in 4 benchmarks (GDPval-AA, HMMT Feb 26, Humanity's Last Exam, IMO-AnswerBench), while Kimi K2.6 is better at 7 benchmarks (BrowseComp, GPQA, SWE-bench Multilingual, SWE-Bench Pro, SWE-Bench Verified, Terminal-Bench 2.0, Toolathlon). Kimi K2.6 shows notably better performance in the majority of benchmarks.

On price, DeepSeek-V4-Flash-Max is roughly 11.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

DeepSeek-V4-Flash-Max also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.

Choose DeepSeek-V4-Flash-Max if…

  • cost matters — it's about 11.5x cheaper per token
  • you process long inputs — it offers a 1,048,576 token context window
  • you want the most recent training data — it shipped Apr 2026

Choose Kimi K2.6 if…

  • you want the strongest raw capability — it leads on 7 of 11 shared benchmarks

Performance Benchmarks

Comparative analysis across standard metrics

11 benchmarks

DeepSeek-V4-Flash-Max outperforms in 4 benchmarks (GDPval-AA, HMMT Feb 26, Humanity's Last Exam, IMO-AnswerBench), while Kimi K2.6 is better at 7 benchmarks (BrowseComp, GPQA, SWE-bench Multilingual, SWE-Bench Pro, SWE-Bench Verified, Terminal-Bench 2.0, Toolathlon).

Kimi K2.6 shows notably better performance in the majority of benchmarks.

Fri Jul 17 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

DeepSeek-V4-Flash-Max costs less

For input processing, DeepSeek-V4-Flash-Max ($0.10/1M tokens) is 7.5x cheaper than Kimi K2.6 ($0.75/1M tokens).

For output processing, DeepSeek-V4-Flash-Max ($0.20/1M tokens) is 17.5x cheaper than Kimi K2.6 ($3.50/1M tokens).

In conclusion, Kimi K2.6 is more expensive than DeepSeek-V4-Flash-Max.*

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

Lowest available price from all providers
Fri Jul 17 2026 • llm-stats.com
DeepSeek
DeepSeek-V4-Flash-Max
Input tokens$0.10
Output tokens$0.20
Best providerDeepinfra
Moonshot AI
Kimi K2.6
Input tokens$0.75
Output tokens$3.50
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue

Model Size

Parameter count comparison

716.0B diff

Kimi K2.6 has 716.0B more parameters than DeepSeek-V4-Flash-Max, making it 252.1% larger.

DeepSeek
DeepSeek-V4-Flash-Max
284.0Bparameters
Moonshot AI
Kimi K2.6
1.0Tparameters
284.0B
DeepSeek-V4-Flash-Max
1000.0B
Kimi K2.6

Context Window

Maximum input and output token capacity

DeepSeek-V4-Flash-Max accepts 1,048,576 input tokens compared to Kimi K2.6's 262,144 tokens. Kimi K2.6 can generate longer responses up to 131,072 tokens, while DeepSeek-V4-Flash-Max is limited to 65,536 tokens.

DeepSeek
DeepSeek-V4-Flash-Max
Input1,048,576 tokens
Output65,536 tokens
Moonshot AI
Kimi K2.6
Input262,144 tokens
Output131,072 tokens
Fri Jul 17 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Kimi K2.6 supports multimodal inputs, whereas DeepSeek-V4-Flash-Max does not.

Kimi K2.6 can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V4-Flash-Max

Text
Images
Audio
Video

Kimi K2.6

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4-Flash-Max is licensed under MIT, while Kimi K2.6 uses Modified MIT License.

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

DeepSeek-V4-Flash-Max

MIT

Open weights

Kimi K2.6

Modified MIT License

Open weights

Release Timeline

When each model was launched

DeepSeek-V4-Flash-Max was released on 2026-04-23, while Kimi K2.6 was released on 2026-04-20.

DeepSeek-V4-Flash-Max is 0 month newer than Kimi K2.6.

DeepSeek-V4-Flash-Max

Apr 23, 2026

2 months ago

3d newer
Kimi K2.6

Apr 20, 2026

2 months ago

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-V4-Flash-Max is available from DeepInfra, DeepSeek, Fireworks, Novita. Kimi K2.6 is available from DeepInfra, Fireworks, Moonshot AI, Novita, Together.

DeepSeek-V4-Flash-Max

deepinfra logo
Deepinfra
Input Price:Input: $0.10/1MOutput Price:Output: $0.20/1M
deepseek logo
DeepSeek
Input Price:Input: $0.14/1MOutput Price:Output: $0.28/1M
fireworks logo
Fireworks
Input Price:Input: $0.14/1MOutput Price:Output: $0.28/1M
novita logo
Novita
Input Price:Input: $0.14/1MOutput Price:Output: $0.28/1M

Kimi K2.6

deepinfra logo
Deepinfra
Input Price:Input: $0.75/1MOutput Price:Output: $3.50/1M
fireworks logo
Fireworks
Input Price:Input: $0.95/1MOutput Price:Output: $4.00/1M
moonshot logo
Unknown Organization
Input Price:Input: $0.95/1MOutput Price:Output: $4.00/1M
novita logo
Novita
Input Price:Input: $0.95/1MOutput Price:Output: $4.00/1M
together logo
Together
Input Price:Input: $1.20/1MOutput Price:Output: $4.50/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Key Takeaways

Larger context window (1,048,576 tokens)
Less expensive input tokens
Less expensive output tokens
Higher GDPval-AA score (40.1% vs 40.1%)
Higher HMMT Feb 26 score (94.8% vs 92.7%)
Higher Humanity's Last Exam score (45.1% vs 36.4%)
Higher IMO-AnswerBench score (88.4% vs 86.0%)
Supports multimodal inputs
Higher BrowseComp score (86.3% vs 73.2%)
Higher GPQA score (90.5% vs 88.1%)
Higher SWE-bench Multilingual score (76.7% vs 73.3%)
Higher SWE-Bench Pro score (58.6% vs 52.6%)
Higher SWE-Bench Verified score (80.2% vs 79.0%)
Higher Terminal-Bench 2.0 score (66.7% vs 56.9%)
Higher Toolathlon score (50.0% vs 47.8%)

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against DeepSeek-V4-Flash-Max and Kimi K2.6 side-by-side, then vote on the output you prefer.

DeepSeek-V4-Flash-Max
✓ Preferred
Kimi K2.6
Open in Playground
AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V4-Flash-Max
Moonshot AI
Kimi K2.6

FAQ

Common questions about DeepSeek-V4-Flash-Max vs Kimi K2.6.

Which is better, DeepSeek-V4-Flash-Max or Kimi K2.6?

Kimi K2.6 shows notably better performance in the majority of benchmarks. DeepSeek-V4-Flash-Max is made by DeepSeek and Kimi K2.6 is made by Moonshot AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek-V4-Flash-Max compare to Kimi K2.6 in benchmarks?

DeepSeek-V4-Flash-Max scores CodeForces: 100.0%, HMMT Feb 26: 94.8%, LiveCodeBench: 91.6%, IMO-AnswerBench: 88.4%, GPQA: 88.1%. Kimi K2.6 scores V*: 96.9%, AIME 2026: 96.4%, MathVision: 93.2%, HMMT Feb 26: 92.7%, GPQA: 90.5%.

Is DeepSeek-V4-Flash-Max cheaper than Kimi K2.6?

DeepSeek-V4-Flash-Max is 7.5x cheaper for input tokens. DeepSeek-V4-Flash-Max costs $0.10/M input and $0.20/M output via deepinfra. Kimi K2.6 costs $0.75/M input and $3.50/M output via deepinfra.

What are the context window sizes for DeepSeek-V4-Flash-Max and Kimi K2.6?

DeepSeek-V4-Flash-Max supports 1.0M tokens and Kimi K2.6 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 DeepSeek-V4-Flash-Max and Kimi K2.6?

Key differences include context window (1.0M vs 262K), input pricing ($0.10 vs $0.75/M), multimodal support (no vs yes), licensing (MIT vs Modified MIT License). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4-Flash-Max and Kimi K2.6?

DeepSeek-V4-Flash-Max is developed by DeepSeek and Kimi K2.6 is developed by Moonshot AI.