GPT-5-Codex vs Qwen3 VL 235B A22B Thinking
Qwen3 VL 235B A22B Thinking comes out ahead, 48 to 42 on our weighted score, and it is the cheaper option too.
OpenAIGPT-5-Codex
42/100- ECI—
- Price$1.25 / $10.00
- Context400K
- Our pick
Alibaba (Qwen)Qwen3 VL 235B A22B Thinking
48/100- ECI—
- Price$0.40 / $4.00
- Context131K
Add a model
Make it a three-way comparison.
Qwen3 VL 235B A22B Thinking is our pick
Qwen3 VL 235B A22B Thinking is the better all-round choice, scoring 48/100 against GPT-5-Codex (42). It leads on price. GPT-5-Codex wins on context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceQwen3 VL 235B A22B ThinkingQwen3 VL 235B A22B Thinking $1.30 · GPT-5-Codex $3.44 per 1M tokens (3:1 blend)
- Longest contextGPT-5-CodexGPT-5-Codex 400,000 · Qwen3 VL 235B A22B Thinking 131,072 tokens
- Widest inputsSame inputsGPT-5-Codex: Text, Images · Qwen3 VL 235B A22B Thinking: Text, Images
- Self-hostingQwen3 VL 235B A22B ThinkingPublishes downloadable weights
| Measure | Weight | GPT-5-Codex | Qwen3 VL 235B A22B Thinking |
|---|---|---|---|
| Price | 50% | 24 | 44 |
| Inputs & features | 30% | 70 | 70 |
| Context window | 20% | 44 | 24 |
| Overall | 100% | 42/100 | 48/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-5-CodexOpenAI | Qwen3 VL 235B A22B ThinkingAlibaba (Qwen) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $1.25 | $0.40 (best) |
| Output | $10.00 | $4.00 (best) |
| Cached input | — | — |
| Blended (3:1) | $3.44 | $1.30 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 3 providers | Median of 9 providers |
| Limits | ||
| Context window | 400,000 tokens (best) | 131,072 tokens |
| Max output | 128,000 tokens (best) | 32,768 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | Yes |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | Yes |
| Tool calling | Yes | Yes |
| Structured output | Yes | Yes |
| Availability | ||
| Weights | Proprietary | Open |
| API model ID | — | — |
| API providers | 3 | 9 (best) |
| Released | Sep 15, 2025 | Sep 23, 2025 |
| Knowledge cutoff | Sep 30, 2024 | Mar 31, 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GPT-5-Codex$32.50
- Qwen3 VL 235B A22B Thinking$12.00
Which should you choose?
Which is better: GPT-5-Codex or Qwen3 VL 235B A22B Thinking?
Qwen3 VL 235B A22B Thinking is the better all-round choice, scoring 48/100 against GPT-5-Codex (42). It leads on price. GPT-5-Codex wins on context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, GPT-5-Codex or Qwen3 VL 235B A22B Thinking?
Qwen3 VL 235B A22B Thinking is cheaper at $0.40 input / $4.00 output per million tokens (median across 9 API providers). GPT-5-Codex costs $1.25 input / $10.00 output per million tokens (median across 3 API providers). At a typical mix of three input tokens to one output token, that is $1.30 per million tokens for Qwen3 VL 235B A22B Thinking versus $3.44 for GPT-5-Codex (2.6× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. GPT-5-Codex has not been scored yet and Qwen3 VL 235B A22B Thinking has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5-Codex and Qwen3 VL 235B A22B Thinking yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.
Which has the bigger context window?
GPT-5-Codex has the largest context window at 400,000 tokens, against 131,072 for Qwen3 VL 235B A22B Thinking. Maximum output per response: GPT-5-Codex up to 128,000, Qwen3 VL 235B A22B Thinking up to 32,768 tokens.
Which can read images, PDFs, audio or video?
GPT-5-Codex accepts text and images; Qwen3 VL 235B A22B Thinking accepts text and images. They handle the same number of input types.
Are any of these open source?
Qwen3 VL 235B A22B Thinking publishes its weights and can be self-hosted; GPT-5-Codex is proprietary.
Which is newer?
Qwen3 VL 235B A22B Thinking is the newest, released Sep 23, 2025. GPT-5-Codex came out Sep 15, 2025. Knowledge cutoff: GPT-5-Codex Sep 30, 2024, Qwen3 VL 235B A22B Thinking Mar 31, 2025.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
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