Qwen3-Next 80B-A3B (Thinking) vs GPT-5-Codex
GPT-5-Codex comes out ahead, 42 to 34 on our weighted score, though Qwen3-Next 80B-A3B (Thinking) is 45% cheaper per token.
Alibaba (Qwen)Qwen3-Next 80B-A3B (Thinking)
34/100- ECI—
- Price$0.50 / $6.00
- Context131K
- Our pick
OpenAIGPT-5-Codex
42/100- ECI—
- Price$1.25 / $10.00
- Context400K
Add a model
Make it a three-way comparison.
GPT-5-Codex is our pick
GPT-5-Codex is the better all-round choice, scoring 42/100 against Qwen3-Next 80B-A3B (Thinking) (34). It leads on inputs & features and context window. Qwen3-Next 80B-A3B (Thinking) wins on price. 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-Next 80B-A3B (Thinking)Qwen3-Next 80B-A3B (Thinking) $1.88 · GPT-5-Codex $3.44 per 1M tokens (3:1 blend)
- Longest contextGPT-5-CodexGPT-5-Codex 400,000 · Qwen3-Next 80B-A3B (Thinking) 131,072 tokens
- Widest inputsGPT-5-CodexQwen3-Next 80B-A3B (Thinking): Text · GPT-5-Codex: Text, Images
- Self-hostingQwen3-Next 80B-A3B (Thinking)Publishes downloadable weights
| Measure | Weight | Qwen3-Next 80B-A3B (Thinking) | GPT-5-Codex |
|---|---|---|---|
| Price | 50% | 37 | 24 |
| Inputs & features | 30% | 35 | 70 |
| Context window | 20% | 24 | 44 |
| Overall | 100% | 34/100 | 42/100 |
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 | Qwen3-Next 80B-A3B (Thinking)Alibaba (Qwen) | GPT-5-CodexOpenAI |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.50 (best) | $1.25 |
| Output | $6.00 (best) | $10.00 |
| Cached input | — | — |
| Blended (3:1) | $1.88 (best) | $3.44 |
| Long-context rate | Same rate | Same rate |
| Price source | Official Alibaba API | Median of 3 providers |
| Limits | ||
| Context window | 131,072 tokens | 400,000 tokens (best) |
| Max output | 32,768 tokens | 128,000 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | Yes |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | Yes |
| Tool calling | Yes | Yes |
| Structured output | No | Yes |
| Availability | ||
| Weights | Open | Proprietary |
| API model ID | qwen3-next-80b-a3b-thinking | — |
| API providers | 10 (best) | 3 |
| Released | Sep 2025 | Sep 15, 2025 |
| Knowledge cutoff | Apr 2025 | Sep 30, 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen3-Next 80B-A3B (Thinking)$17.00
- GPT-5-Codex$32.50
Which should you choose?
Which is better: Qwen3-Next 80B-A3B (Thinking) or GPT-5-Codex?
GPT-5-Codex is the better all-round choice, scoring 42/100 against Qwen3-Next 80B-A3B (Thinking) (34). It leads on inputs & features and context window. Qwen3-Next 80B-A3B (Thinking) wins on price. 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, Qwen3-Next 80B-A3B (Thinking) or GPT-5-Codex?
Qwen3-Next 80B-A3B (Thinking) is cheaper at $0.50 input / $6.00 output per million tokens (official Alibaba API price). 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 $1.88 per million tokens for Qwen3-Next 80B-A3B (Thinking) versus $3.44 for GPT-5-Codex (1.8× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Qwen3-Next 80B-A3B (Thinking) has not been scored yet and GPT-5-Codex has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3-Next 80B-A3B (Thinking) and GPT-5-Codex 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-Next 80B-A3B (Thinking). Maximum output per response: Qwen3-Next 80B-A3B (Thinking) up to 32,768, GPT-5-Codex up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Qwen3-Next 80B-A3B (Thinking) accepts text; GPT-5-Codex accepts text and images. GPT-5-Codex handles the widest range of inputs.
Are any of these open source?
Qwen3-Next 80B-A3B (Thinking) publishes its weights and can be self-hosted; GPT-5-Codex is proprietary.
Which is newer?
GPT-5-Codex is the newest, released Sep 15, 2025. Qwen3-Next 80B-A3B (Thinking) came out Sep 2025. Knowledge cutoff: Qwen3-Next 80B-A3B (Thinking) Apr 2025, GPT-5-Codex Sep 30, 2024.
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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