GPT-5.2 Codex vs Qwen3.8 Flash
Qwen3.8 Flash leads the LLM Stats Score 49.6 to 34.6. Qwen3.8 Flash is 20.9x cheaper per token.
OpenAI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Qwen3.8 Flash leads the overall LLM Stats Score 49.6 to 34.6, ranking #16 overall.
In the 1 individual benchmarks reported for both models, Qwen3.8 Flash wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen3.8 Flash is roughly 20.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3.8 Flash also accepts a larger context window (1,000,000 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 GPT-5.2 Codex
- you want predictable pricing at $1.75/M input and $14.00/M output
Choose Qwen3.8 Flash
- overall performance matters — it scores 49.6 and ranks #16 on LLM Stats
- your work emphasizes reasoning and agents — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- cost matters — it's about 20.9x cheaper per token
- you process long inputs — it offers a 1,000,000 token context window
- you want the most recent training data — it shipped Aug 2026
At a glance
The differences that matter most.
Individual benchmarks
3 reported for GPT-5.2 Codex · 22 for Qwen3.8 Flash
GPT-5.2 Codex outperforms in 0 benchmarks, while Qwen3.8 Flash is better at 1 benchmark (SWE-Bench Pro).
Qwen3.8 Flash 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, GPT-5.2 Codex ($1.75/1M tokens) is 11.7x more expensive than Qwen3.8 Flash ($0.15/1M tokens).
For output processing, GPT-5.2 Codex ($14.00/1M tokens) is 29.8x more expensive than Qwen3.8 Flash ($0.47/1M tokens).
In conclusion, GPT-5.2 Codex is more expensive than Qwen3.8 Flash.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Qwen3.8 Flash accepts 1,000,000 input tokens compared to GPT-5.2 Codex's 400,000 tokens. Qwen3.8 Flash can generate longer responses up to 131,072 tokens, while GPT-5.2 Codex is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
Both GPT-5.2 Codex and Qwen3.8 Flash support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GPT-5.2 Codex
Qwen3.8 Flash
License
Usage and distribution terms
Both models are licensed under proprietary licenses.
Both models have usage restrictions defined by their respective organizations.
Proprietary
Closed source
Proprietary
Closed source
Release Timeline
When each model was launched
GPT-5.2 Codex was released on 2026-01-14, while Qwen3.8 Flash was released on 2026-08-26.
Qwen3.8 Flash is 7 months newer than GPT-5.2 Codex.
Jan 14, 2026
7 months ago
Aug 26, 2026
5 days ago
7mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GPT-5.2 Codex is available from OpenAI. Qwen3.8 Flash is available from Novita.
GPT-5.2 Codex
Qwen3.8 Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT-5.2 Codex and Qwen3.8 Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT-5.2 Codex vs Qwen3.8 Flash.