Ling 3.1 Flash vs GPT-5.3 Codex: which should you choose in 2026?
Ling 3.1 Flash (by inclusionAI) and GPT-5.3 Codex (by OpenAI) 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.
Ling 3.1 Flash has no token charge in the catalog; the other model costs $4.81 per 1M tokens on a blended 3:1 input/output basis.
GPT-5.3 Codex has the larger context window (400,000 tokens vs 262,144 tokens).
Choose Ling 3.1 Flash if…
- • lower blended API cost matters for your workload
Choose GPT-5.3 Codex if…
- • you work with longer documents, transcripts, or codebases
- • you need image inputs
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 | Ling 3.1 Flash | GPT-5.3 Codex |
|---|---|---|
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 Oct 5, 2026.
Pricing, capabilities, and model facts
| Feature | Ling 3.1 Flash | GPT-5.3 Codex |
|---|---|---|
| Context & model facts | ||
| Developer | inclusionAI | OpenAI |
| API provider | inclusionAI | OpenAI |
| Input context | 262,144 tokens | 400,000 tokens |
| Maximum output | 32,768 tokens | 128,000 tokens |
| Released | — | Feb 5, 2026 |
| Added to Writingmate | Oct 2, 2026 | Feb 24, 2026 |
| License | Not available | Proprietary |
| Knowledge cutoff | — | Not disclosed |
| Capabilities | ||
| Inputs | Text | Text, Image, File |
| Outputs | Text | Text |
| Provider endpoint accepts tool parameters | Yes | Yes |
| Reasoning | Yes | Yes |
| Vision | No | Yes |
| Image Generation | No | No |
| Video Generation | No | No |
| API pricing | ||
| Input (per 1M tokens) | $0.00 | $1.75 |
| Output (per 1M tokens) | $0.00 | $14.00 |
| Blended 3:1 input/output | $0.00 | $4.81 |
| 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 Oct 2, 2026.
Why Pay for Multiple Subscriptions?
Comparing Ling 3.1 Flash from inclusionAI with GPT-5.3 Codex from OpenAI? Instead of managing separate API keys and subscriptions, get both with Writingmate.
| Plan | Price | Ling 3.1 Flash | GPT-5.3 Codex | AI Images | AI Video |
|---|---|---|---|---|---|
Writingmate Pro Most popular | $20/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | VEO 3.1, Kling 3.0 |
Writingmate Ultimate Power users | $60/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | VEO 3.1, Kling 3.0 |
Ling 3.1 Flash vs GPT-5.3 Codex FAQ
Which is better, Ling 3.1 Flash or GPT-5.3 Codex?
There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Ling 3.1 Flash has no token charge in the catalog; the other model costs $4.81 per 1M tokens on a blended 3:1 input/output basis. 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?
Ling 3.1 Flash has no token charge in the catalog; the other model costs $4.81 per 1M tokens on a blended 3:1 input/output basis.
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 Ling 3.1 Flash and GPT-5.3 Codex?
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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