Pareto vs GPT-5.6 Terra vs Kimi K3.1 Pro
Too close to call on our weighted score (Kimi K3.1 Pro 46, GPT-5.6 Terra 45, Pareto 33). The right pick depends on what you value most.
Unbiased
Pareto
33/100- ECI—
- Price$2.57 / $7.72
- Context262K
OpenAIGPT-5.6 Terra
45/100- ECI160.1
- Price$2.00 / $12.00
- Context1.05M
Moonshot AIKimi K3.1 Pro
46/100- ECI—
- Price$2.00 / $10.00
- Context1.05M
Too close to call
It is close. Our weighted score puts them within 1 points (Kimi K3.1 Pro 46/100, GPT-5.6 Terra 45/100, Pareto 33/100), so choose by what matters most for your work: Pareto 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 priceParetoPareto $3.86 · Kimi K3.1 Pro $4.00 · GPT-5.6 Terra $4.50 per 1M tokens (3:1 blend)
- Longest contextGPT-5.6 Terra and Kimi K3.1 ProGPT-5.6 Terra 1,050,000 · Kimi K3.1 Pro 1,048,576 · Pareto 262,144 tokens
- Widest inputsGPT-5.6 Terra and Kimi K3.1 ProPareto: Text, Images · GPT-5.6 Terra: Text, Images, PDFs · Kimi K3.1 Pro: Text, Images, Video
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | Pareto | GPT-5.6 Terra | Kimi K3.1 Pro |
|---|---|---|---|---|
| Price | 50% | 21 | 18 | 20 |
| Inputs & features | 30% | 50 | 80 | 80 |
| Context window | 20% | 37 | 61 | 61 |
| Overall | 100% | 33/100 | 45/100 | 46/100 |
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 | ParetoUnbiased | GPT-5.6 TerraOpenAI | Kimi K3.1 ProMoonshot AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 160.1 | — |
| ECI rank | — | #15 of 165 | — |
| GPQA DiamondGraduate-level science questions | — | 93.3% | — |
| FrontierMath Tiers 1–3Research-level mathematics | — | 86.0% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 99.7% | — |
| SimpleQA VerifiedShort factual questions | — | 43.2% | — |
| Price per million tokens | |||
| Input | $2.57 | $2.00 (best) | $2.00 (best) |
| Output | $7.72 (best) | $12.00 | $10.00 |
| Cached input | — | $0.20 | $0.20 |
| Blended (3:1) | $3.86 (best) | $4.50 | $4.00 |
| Long-context rate | Same rate | Over 272K: $4.00 / $18.00 | Same rate |
| Price source | Median of 2 providers | Official OpenAI API | Official Moonshot AI API |
| Limits | |||
| Context window | 262,144 tokens | 1,050,000 tokens (best) | 1,048,576 tokens |
| Max output | 131,072 tokens (best) | 128,000 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | No | Yeslow · medium · high · xhigh · max | Yeslow · high · max |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | — | gpt-5.6-terra | kimi-k3.1-pro |
| API providers | 2 | 45 (best) | 17 |
| Released | Sep 17, 2026 | Jul 9, 2026 | Oct 23, 2026 |
| Knowledge cutoff | — | Feb 16, 2026 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Pareto$41.16
- GPT-5.6 Terra$44.00
- Kimi K3.1 Pro$40.00
Which should you choose?
Which is better: Pareto, GPT-5.6 Terra or Kimi K3.1 Pro?
It is close. Our weighted score puts them within 1 points (Kimi K3.1 Pro 46/100, GPT-5.6 Terra 45/100, Pareto 33/100), so choose by what matters most for your work: Pareto 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, Pareto, GPT-5.6 Terra or Kimi K3.1 Pro?
Pareto is cheaper at $2.57 input / $7.72 output per million tokens (median across 2 API providers). Kimi K3.1 Pro costs $2.00 input / $10.00 output per million tokens (official Moonshot AI API price); GPT-5.6 Terra costs $2.00 input / $12.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $3.86 per million tokens for Pareto versus $4.00 for Kimi K3.1 Pro (1× as much) and $4.50 for GPT-5.6 Terra (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Pareto has not been scored yet, GPT-5.6 Terra has an ECI of 160.1 and Kimi K3.1 Pro has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Pareto, GPT-5.6 Terra and Kimi K3.1 Pro yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
GPT-5.6 Terra and Kimi K3.1 Pro have the largest context windows (1,050,000 and 1,048,576 tokens), against 262,144 for Pareto. Maximum output per response: Pareto up to 131,072, GPT-5.6 Terra up to 128,000, Kimi K3.1 Pro up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Pareto accepts text and images; GPT-5.6 Terra accepts text, images and PDFs; Kimi K3.1 Pro accepts text, images and video. GPT-5.6 Terra handles the widest range of inputs.
Are any of these open source?
No. Pareto, GPT-5.6 Terra and Kimi K3.1 Pro are proprietary and only available through APIs and apps.
Which is newer?
Kimi K3.1 Pro is the newest, released Oct 23, 2026. Pareto came out Sep 17, 2026; GPT-5.6 Terra came out Jul 9, 2026. Knowledge cutoff: GPT-5.6 Terra Feb 16, 2026.
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 Nov 2, 2026.
