Ming Image 0.1 Design Layer vs GPT-5.6 TerraWhich Is Better in 2026?
Ming Image 0.1 Design Layer vs GPT-5.6 Terra: which should you choose in 2026?
Ming Image 0.1 Design Layer (by inclusionAI) and GPT-5.6 Terra (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.
Choose Ming Image 0.1 Design Layer if…
- • you need image generation
Choose GPT-5.6 Terra if…
- • you need a model with explicit reasoning support
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 | Ming Image 0.1 Design Layer | GPT-5.6 Terra |
|---|---|---|
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 | — | 45.6% Artificial Analysis |
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 | — | — |
A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state. Mean task reward | — | — |
A long-horizon software-engineering benchmark with 113 original tasks graded by hand-written tests. Pass@1 | — | 70.0%±3.0 DataCurve |
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 | — | — |
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 | — | 78.4%±1.3 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 | — | 92.5% Artificial Analysis |
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 | — | 42.9% Artificial Analysis |
A contamination-resistant benchmark refreshed on a fixed release cadence. Scores from different LiveBench releases must never be compared as the same protocol. Mean of category averages | — | 77.9% LiveBench |
Benchmark sources
- AutomationBench-AA: Agentic SaaS Workflow Benchmark
- DeepSWE 1.1 Leaderboard
- terminal-bench@2.1 Leaderboard
- GPQA Diamond Benchmark Leaderboard
- Humanity's Last Exam Benchmark Leaderboard
- LiveBench 2026-06-25 Leaderboard
Reviewed evidence last updated 2026-08-09.
Pricing, capabilities, and model facts
| Feature | Ming Image 0.1 Design Layer | GPT-5.6 Terra |
|---|---|---|
| Context & model facts | ||
| Developer | inclusionAI | OpenAI |
| API provider | inclusionAI | OpenAI |
| Input context | — | 1,050,000 tokens |
| Maximum output | — | 128,000 tokens |
| Released | — | — |
| Added to Writingmate | Sep 23, 2026 | Jul 9, 2026 |
| License | Not available | Not available |
| Knowledge cutoff | — | 2026-02-16 |
| Capabilities | ||
| Inputs | Text, Image | File, Image, Text |
| Outputs | Image | Text |
| Provider endpoint accepts tool parameters | No | Yes |
| Reasoning | No | Yes |
| Vision | Yes | Yes |
| Image Generation | Yes | No |
| Video Generation | No | No |
| 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
- Writingmate model catalog (pricing, limits, and availability)
Catalog data last updated Sep 23, 2026.
Why Pay for Multiple Subscriptions?
Comparing Ming Image 0.1 Design Layer from inclusionAI with GPT-5.6 Terra from OpenAI? Instead of managing separate API keys and subscriptions, get both with Writingmate.
| Plan | Price | Ming Image 0.1 Design Layer | GPT-5.6 Terra | 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 |
Ming Image 0.1 Design Layer vs GPT-5.6 Terra FAQ
Which is better, Ming Image 0.1 Design Layer or GPT-5.6 Terra?
There are not enough shared, protocol-compatible benchmark results to declare a performance leader.
Which model is cheaper to use through an API?
A comparable blended token price is not available for both models. Check the API pricing rows for the values that are currently published.
Which model supports more context?
Comparable context-window data is not available for both models.
Can I switch between Ming Image 0.1 Design Layer and GPT-5.6 Terra?
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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