DeepSeek-V4-Flash-0731 vs GPT-5.2 Codex
Comparing DeepSeek-V4-Flash-0731 and GPT-5.2 Codex across benchmarks, pricing, and capabilities.
DeepSeek · OpenAI · Updated for 2026
Which is better?
DeepSeek-V4-Flash-0731 and GPT-5.2 Codex trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, DeepSeek-V4-Flash-0731 is roughly 42.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Flash-0731 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-Flash-0731
- cost matters — it's about 42.8x 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 Jul 2026
- you need open weights you can self-host or fine-tune
Choose GPT-5.2 Codex
- you want predictable pricing at $1.75/M input and $14.00/M output
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Flash-0731 and GPT-5.2 Codexdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Flash-0731 ($0.09/1M tokens) is 19.4x cheaper than GPT-5.2 Codex ($1.75/1M tokens).
For output processing, DeepSeek-V4-Flash-0731 ($0.18/1M tokens) is 77.8x cheaper than GPT-5.2 Codex ($14.00/1M tokens).
In conclusion, GPT-5.2 Codex is more expensive than DeepSeek-V4-Flash-0731.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
DeepSeek-V4-Flash-0731 accepts 1,048,576 input tokens compared to GPT-5.2 Codex's 400,000 tokens. GPT-5.2 Codex can generate longer responses up to 128,000 tokens, while DeepSeek-V4-Flash-0731 is limited to 65,536 tokens.
Input Capabilities
Supported data types and modalities
GPT-5.2 Codex supports multimodal inputs, whereas DeepSeek-V4-Flash-0731 does not.
GPT-5.2 Codex can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Flash-0731
GPT-5.2 Codex
License
Usage and distribution terms
DeepSeek-V4-Flash-0731 is licensed under MIT, while GPT-5.2 Codex 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-0731 was released on 2026-07-31, while GPT-5.2 Codex was released on 2026-01-14.
DeepSeek-V4-Flash-0731 is 7 months newer than GPT-5.2 Codex.
Jul 31, 2026
3 weeks ago
6mo newerJan 14, 2026
7 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-Flash-0731 is available from DeepInfra, Novita, Fireworks. GPT-5.2 Codex is available from OpenAI.
DeepSeek-V4-Flash-0731
GPT-5.2 Codex
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0731 and GPT-5.2 Codex side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Flash-0731 vs GPT-5.2 Codex.