GPT-5.3 Codex vs Qwen3 Coder Next: which should you choose in 2026?
GPT-5.3 Codex (by OpenAI) and Qwen3 Coder Next (by Qwen) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.
There are not enough shared, protocol-compatible benchmark results to declare a performance leader.
Qwen3 Coder Next is about 16.6× cheaper on a blended 3:1 input/output basis ($0.29 vs $4.81 per 1M tokens).
GPT-5.3 Codex has the larger context window (400,000 tokens vs 262,144 tokens).
Choose GPT-5.3 Codex if…
- • you work with longer documents, transcripts, or codebases
- • you need a model with explicit reasoning support
- • you need image inputs
Choose Qwen3 Coder Next if…
- • lower blended API cost matters for your workload
The benchmark count includes only results measured with a matching benchmark version and protocol. Arena scores are shown separately. Missing, preliminary, and incompatible data is not treated as a controlled win. Published point-score comparisons are labeled separately when protocol details are incomplete. For text-output models, the verdict also compares token pricing and context windows.
Performance benchmarks
Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.
| Benchmark | GPT-5.3 Codex | Qwen3 Coder Next |
|---|---|---|
A 657-task benchmark of multi-step work across simulated SaaS applications in six business domains. The Artificial Analysis protocol reports a guardrail-aware score and is distinct from both the public Zapier split and the unrelated dynamic AutoBench framework. Guardrail-aware score | — | — |
A benchmark of difficult, verifiable information-seeking questions designed to measure an agent's ability to locate hard-to-find facts through web browsing. Accuracy | 77.3% OpenAI report | — |
A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state. Mean task reward | 74.0% OpenAI report | — |
A contamination-resistant software-engineering benchmark with long-horizon tasks across multiple programming languages. Public and private splits are distinct protocols. Resolved | 56.8% OpenAI report | — |
A human-validated subset of real GitHub issues used to measure whether a coding agent can produce repository patches that resolve the associated tests. Resolved | — | — |
The individual-contributor Diamond subset of SWE-Lancer, which evaluates economically valuable real-world software-engineering tasks. Tasks completed | 81.4% OpenAI report | — |
Version 2.0 of the benchmark for completing realistic tasks in terminal environments. Results must not be merged with Terminal-Bench 2.1. Pass rate | 77.3% OpenAI report | — |
Version 2.1 of the benchmark for completing realistic tasks in terminal environments. Harness, resource limits, and attempt count are part of the protocol. Mean task success | 79.1% Terminal-Bench | — |
A dynamic LLM evaluation framework in which models generate questions, answer them, and participate in reciprocal peer assessment. AutoBench is distinct from Zapier's AutomationBench. Weighted peer-assessment score | — | — |
The highest-quality subset of Graduate-Level Google-Proof Q&A, designed to test expert-level scientific reasoning in biology, physics, and chemistry. Accuracy | 92.6% OpenAI report | — |
A 2,500-question expert-level benchmark spanning dozens of academic fields. Tool-assisted and no-tools results are separate protocols and must not be merged. Accuracy | — | — |
Cybersecurity CTFs Capture-the-flag challenges used to evaluate an agent's practical cybersecurity task performance. Challenges completed | 77.6% OpenAI report | — |
| Arena preference scores | ||
Human preference score for code and web development | Ambiguous / conflicting rows | — |
Benchmark sources
- Arena AI leaderboard
- Introducing GPT-5.4
- Introducing GPT-5.3-Codex
- Terminal-Bench 2.1 Verified Leaderboard
Reviewed evidence last updated 2026-08-09. Arena data last refreshed Aug 18, 2026.
Pricing, capabilities, and model facts
| Feature | GPT-5.3 Codex | Qwen3 Coder Next |
|---|---|---|
| Context & model facts | ||
| Developer | OpenAI | Qwen |
| API provider | OpenAI | Qwen |
| Input context | 400,000 tokens | 262,144 tokens |
| Maximum output | 128,000 tokens | 262,144 tokens |
| Released | Feb 5, 2026 | — |
| Added to Writingmate | Feb 24, 2026 | Feb 4, 2026 |
| License | Proprietary | Not available |
| Knowledge cutoff | Not disclosed | — |
| Capabilities | ||
| Inputs | Text, Image, File | Text |
| Outputs | Text | Text |
| Provider endpoint accepts tool parameters | Yes | Yes |
| Reasoning | Yes | No |
| Vision | Yes | No |
| Image Generation | No | No |
| Video Generation | No | No |
| API pricing | ||
| Input (per 1M tokens) | $1.75 | $0.12 |
| Output (per 1M tokens) | $14.00 | $0.80 |
| Blended 3:1 input/output | $4.81 | $0.29 |
| API performance | ||
| p95 latency | Not measured | Not measured |
| Output throughput | Not measured | Not measured |
| Writingmate shows API performance only when both models have enough observations from the same measurement window, prompt profile, and provider. Third-party latency values are not copied into this table. | ||
Data sources
- Introducing GPT-5.3-Codex
- Writingmate model catalog (pricing, limits, and availability)
Catalog data last updated Feb 24, 2026.
Why Pay for Multiple Subscriptions?
Comparing GPT-5.3 Codex from OpenAI with Qwen3 Coder Next from Qwen? Instead of managing separate API keys and subscriptions, get both with Writingmate.
| Plan | Price | GPT-5.3 Codex | Qwen3 Coder Next | AI Images | AI Video |
|---|---|---|---|---|---|
Writingmate Pro Most popular | $20/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
Writingmate Ultimate Power users | $60/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
GPT-5.3 Codex vs Qwen3 Coder Next FAQ
Which is better, GPT-5.3 Codex or Qwen3 Coder Next?
There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Qwen3 Coder Next is about 16.6× cheaper on a blended 3:1 input/output basis ($0.29 vs $4.81 per 1M tokens). GPT-5.3 Codex has the larger context window (400,000 tokens vs 262,144 tokens).
Which model is cheaper to use through an API?
Qwen3 Coder Next is about 16.6× cheaper on a blended 3:1 input/output basis ($0.29 vs $4.81 per 1M tokens).
Which model supports more context?
GPT-5.3 Codex has the larger context window (400,000 tokens vs 262,144 tokens).
Can I switch between GPT-5.3 Codex and Qwen3 Coder Next?
Yes. Use the model selector on this page to open any current Writingmate model comparison. You can also run the same prompt with both models in Writingmate.
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