DeepSeek-V4-Flash-0423 vs Qwen3.7 Max
Qwen3.7 Max leads the LLM Stats Score 45.4 to 36.1. DeepSeek-V4-Flash-0423 is 16.7x cheaper per token.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Qwen3.7 Max leads the overall LLM Stats Score 45.4 to 36.1, ranking #32 overall.
In the 11 individual benchmarks reported for both models, Qwen3.7 Max wins 10; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V4-Flash-0423 is roughly 16.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Flash-0423 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-Flash-0423
- cost matters — it's about 16.7x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you need open weights you can self-host or fine-tune
Choose Qwen3.7 Max
- overall performance matters — it scores 45.4 and ranks #32 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 10 of 11 exact shared results
- you want the most recent training data — it shipped May 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
19 reported for DeepSeek-V4-Flash-0423 · 42 for Qwen3.7 Max
DeepSeek-V4-Flash-0423 outperforms in 1 benchmarks (MathArena Apex), while Qwen3.7 Max is better at 10 benchmarks (GPQA, HMMT Feb 26, Humanity's Last Exam, IMO-AnswerBench, MCP Atlas, MMLU-Pro, SWE-bench Multilingual, SWE-Bench Pro, SWE-Bench Verified, Terminal-Bench 2.0).
Qwen3.7 Max significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Flash-0423 ($0.09/1M tokens) is 13.9x cheaper than Qwen3.7 Max ($1.25/1M tokens).
For output processing, DeepSeek-V4-Flash-0423 ($0.18/1M tokens) is 20.8x cheaper than Qwen3.7 Max ($3.75/1M tokens).
In conclusion, Qwen3.7 Max is more expensive than DeepSeek-V4-Flash-0423.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
DeepSeek-V4-Flash-0423 accepts 1,048,576 input tokens compared to Qwen3.7 Max's 1,000,000 tokens. DeepSeek-V4-Flash-0423 can generate longer responses up to 1,048,576 tokens, while Qwen3.7 Max is limited to 65,536 tokens.
License
Usage and distribution terms
DeepSeek-V4-Flash-0423 is licensed under MIT, while Qwen3.7 Max uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
DeepSeek-V4-Flash-0423 was released on 2026-04-23, while Qwen3.7 Max was released on 2026-05-19.
Qwen3.7 Max is 1 month newer than DeepSeek-V4-Flash-0423.
Apr 23, 2026
5 months ago
May 19, 2026
4 months ago
3w newerKnowledge 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-Flash-0423 is available from DeepInfra, Novita. Qwen3.7 Max is available from Novita, DeepInfra, Together.
DeepSeek-V4-Flash-0423
Qwen3.7 Max
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0423 and Qwen3.7 Max side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Flash-0423 vs Qwen3.7 Max.