DeepSeek-V4-Pro-0813 vs Qwen3.6-27B
DeepSeek-V4-Pro-0813 significantly outperforms across most benchmarks. DeepSeek-V4-Pro-0813 is 2.5x cheaper per token.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek-V4-Pro-0813 outperforms in 2 benchmarks (Humanity's Last Exam, NL2Repo), while Qwen3.6-27B is better at 0 benchmarks. DeepSeek-V4-Pro-0813 significantly outperforms across most benchmarks.
On price, DeepSeek-V4-Pro-0813 is roughly 2.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Pro-0813 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current benchmark, pricing, and model metadata for 2026.
Choose DeepSeek-V4-Pro-0813
- you want the strongest raw capability — it leads on 2 of 2 shared benchmarks
- cost matters — it's about 2.5x 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 Aug 2026
Choose Qwen3.6-27B
- you want predictable pricing at $0.60/M input and $3.60/M output
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Pro-0813 outperforms in 2 benchmarks (Humanity's Last Exam, NL2Repo), while Qwen3.6-27B is better at 0 benchmarks.
DeepSeek-V4-Pro-0813 significantly outperforms across most benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Pro-0813 ($0.43/1M tokens) is 1.4x cheaper than Qwen3.6-27B ($0.60/1M tokens).
For output processing, DeepSeek-V4-Pro-0813 ($0.87/1M tokens) is 4.1x cheaper than Qwen3.6-27B ($3.60/1M tokens).
In conclusion, Qwen3.6-27B is more expensive than DeepSeek-V4-Pro-0813.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Pro-0813 has 1572.2B more parameters than Qwen3.6-27B, making it 5659.2% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Pro-0813 accepts 1,048,576 input tokens compared to Qwen3.6-27B's 262,144 tokens. DeepSeek-V4-Pro-0813 can generate longer responses up to 393,216 tokens, while Qwen3.6-27B is limited to 65,536 tokens.
Input Capabilities
Supported data types and modalities
Qwen3.6-27B supports multimodal inputs, whereas DeepSeek-V4-Pro-0813 does not.
Qwen3.6-27B can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Pro-0813
Qwen3.6-27B
License
Usage and distribution terms
DeepSeek-V4-Pro-0813 is licensed under MIT, while Qwen3.6-27B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
DeepSeek-V4-Pro-0813 was released on 2026-08-13, while Qwen3.6-27B was released on 2026-04-21.
DeepSeek-V4-Pro-0813 is 4 months newer than Qwen3.6-27B.
Aug 13, 2026
1 weeks ago
3mo newerApr 21, 2026
4 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V4-Pro-0813 is available from DeepSeek, DeepInfra, Novita, Together. Qwen3.6-27B is available from Novita.
DeepSeek-V4-Pro-0813
Qwen3.6-27B
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Pro-0813 and Qwen3.6-27B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Pro-0813 vs Qwen3.6-27B.