DeepSeek-V4.1-Flash-1017 vs Qwen3-Coder
Comparing DeepSeek-V4.1-Flash-1017 and Qwen3-Coder across benchmarks, pricing, and capabilities.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash-1017 and Qwen3-Coder trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, Qwen3-Coder is roughly 2.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4.1-Flash-1017 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 LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V4.1-Flash-1017
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Oct 2026
Choose Qwen3-Coder
- cost matters — it's about 2.9x cheaper per token
At a glance
The differences that matter most.
Individual benchmarks
11 reported for DeepSeek-V4.1-Flash-1017 · 0 for Qwen3-Coder
DeepSeek-V4.1-Flash-1017 and Qwen3-Coderdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4.1-Flash-1017 ($0.30/1M tokens) is 1.7x more expensive than Qwen3-Coder ($0.18/1M tokens).
For output processing, DeepSeek-V4.1-Flash-1017 ($1.20/1M tokens) is 6.7x more expensive than Qwen3-Coder ($0.18/1M tokens).
In conclusion, DeepSeek-V4.1-Flash-1017 is more expensive than Qwen3-Coder.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash-1017 has 283.2B more parameters than Qwen3-Coder, making it 59.0% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4.1-Flash-1017 accepts 1,048,576 input tokens compared to Qwen3-Coder's 256,000 tokens. DeepSeek-V4.1-Flash-1017 can generate longer responses up to 393,216 tokens, while Qwen3-Coder is limited to 256,000 tokens.
Input capabilities
Documented input modalities across available providers
DeepSeek-V4.1-Flash-1017 supports multimodal inputs, whereas Qwen3-Coder does not.
DeepSeek-V4.1-Flash-1017 can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4.1-Flash-1017
Qwen3-Coder
License
Usage and distribution terms
DeepSeek-V4.1-Flash-1017 is licensed under MIT, while Qwen3-Coder 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-V4.1-Flash-1017 was released on 2026-10-17, while Qwen3-Coder was released on 2025-01-01.
DeepSeek-V4.1-Flash-1017 is 22 months newer than Qwen3-Coder.
Oct 17, 2026
2 days ago
1.8yr newerJan 1, 2025
1.8 years 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-V4.1-Flash-1017 is available from DeepSeek. Qwen3-Coder is available from DeepInfra, Fireworks.
DeepSeek-V4.1-Flash-1017
Qwen3-Coder
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash-1017 and Qwen3-Coder side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash-1017 vs Qwen3-Coder.
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