GPT-5.6 Terra vs GLM-5.3 vs Kimi K3
Too close to call on our weighted score (GPT-5.6 Terra 67, Kimi K3 65, GLM-5.3 63). The right pick depends on what you value most.
OpenAIGPT-5.6 Terra
67/100- ECI160.1
- Price$2.00 / $12.00
- Context1.05M
Z.ai (Zhipu)GLM-5.3
63/100- ECI156.2
- Price$1.40 / $4.40
- Context1M
Moonshot AIKimi K3
65/100- ECI158.3
- Price$3.00 / $15.00
- Context1.05M
Too close to call
It is close. Our weighted score puts them within 2 points (GPT-5.6 Terra 67/100, Kimi K3 65/100, GLM-5.3 63/100), so choose by what matters most for your work: GPT-5.6 Terra for raw capability and GLM-5.3 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGPT-5.6 TerraCapabilities Index (ECI): GPT-5.6 Terra 160.1 · Kimi K3 158.3 · GLM-5.3 156.2
- Lowest priceGLM-5.3GLM-5.3 $2.15 · GPT-5.6 Terra $4.50 · Kimi K3 $6.00 per 1M tokens (3:1 blend)
- Longest contextGPT-5.6 Terra and Kimi K3GPT-5.6 Terra 1,050,000 · Kimi K3 1,048,576 · GLM-5.3 1,000,000 tokens
- Widest inputsGPT-5.6 Terra and Kimi K3GPT-5.6 Terra: Text, Images, PDFs · GLM-5.3: Text · Kimi K3: Text, Images, Video
- Self-hostingGLM-5.3 and Kimi K3Publishes downloadable weights
| Measure | Weight | GPT-5.6 Terra | GLM-5.3 | Kimi K3 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 89 | 84 | 87 |
| Price | 25% | 18 | 33 | 12 |
| Inputs & features | 15% | 80 | 45 | 80 |
| Context window | 10% | 61 | 60 | 61 |
| Overall | 100% | 67/100 | 63/100 | 65/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-5.6 TerraOpenAI | GLM-5.3Z.ai (Zhipu) | Kimi K3Moonshot AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 160.1 (best) | 156.2 | 158.3 |
| ECI rank | #15 of 165 (best) | #33 of 165 | #20 of 165 |
| GPQA DiamondGraduate-level science questions | 93.3% (best) | 90.9% | 93.1% |
| FrontierMath Tiers 1–3Research-level mathematics | 86.0% (best) | 68.8% | 72.2% |
| OTIS Mock AIME 2024–2025Competition mathematics | 99.7% (best) | 91.1% | 97.2% |
| SimpleQA VerifiedShort factual questions | 43.2% | 41.0% | 50.6% (best) |
| Price per million tokens | |||
| Input | $2.00 | $1.40 (best) | $3.00 |
| Output | $12.00 | $4.40 (best) | $15.00 |
| Cached input | $0.20 (best) | $0.26 | $0.30 |
| Blended (3:1) | $4.50 | $2.15 (best) | $6.00 |
| Long-context rate | Over 272K: $4.00 / $18.00 | Same rate | Same rate |
| Price source | Official OpenAI API | Official Z.AI API | Official Moonshot AI API |
| Limits | |||
| Context window | 1,050,000 tokens (best) | 1,000,000 tokens | 1,048,576 tokens |
| Max output | 128,000 tokens | 131,072 tokens (best) | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | Yes | No | No |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | Yeslow · medium · high · xhigh · max | Yeslow · high · max | Yeslow · high · max |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | gpt-5.6-terra | glm-5.3 | kimi-k3 |
| API providers | 45 | 83 (best) | 76 |
| Released | Jul 9, 2026 | Aug 14, 2026 | Jul 16, 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.
- GPT-5.6 Terra$44.00
- GLM-5.3$22.80
- Kimi K3$60.00
Which should you choose?
Which is better: GPT-5.6 Terra, GLM-5.3 or Kimi K3?
It is close. Our weighted score puts them within 2 points (GPT-5.6 Terra 67/100, Kimi K3 65/100, GLM-5.3 63/100), so choose by what matters most for your work: GPT-5.6 Terra for raw capability and GLM-5.3 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GPT-5.6 Terra, GLM-5.3 or Kimi K3?
GLM-5.3 is cheaper at $1.40 input / $4.40 output per million tokens (official Z.AI API price). GPT-5.6 Terra costs $2.00 input / $12.00 output per million tokens (official OpenAI API price); Kimi K3 costs $3.00 input / $15.00 output per million tokens (official Moonshot AI API price). At a typical mix of three input tokens to one output token, that is $2.15 per million tokens for GLM-5.3 versus $4.50 for GPT-5.6 Terra (2.1× as much) and $6.00 for Kimi K3 (2.8× as much).
Which scores higher on benchmarks?
GPT-5.6 Terra scores higher on the Capabilities Index (ECI): GPT-5.6 Terra 160.1 (#15 of 165), Kimi K3 158.3 (#20 of 165) and GLM-5.3 156.2 (#33 of 165). The confidence ranges of the top two overlap (157.8–163.4 vs 155.8–161.4), so treat the gap as small. On individual benchmarks: GPQA Diamond — GPT-5.6 Terra 93.3%, Kimi K3 93.1%, GLM-5.3 90.9%; FrontierMath Tiers 1–3 — GPT-5.6 Terra 86.0%, Kimi K3 72.2%, GLM-5.3 68.8%; OTIS Mock AIME 2024–2025 — GPT-5.6 Terra 99.7%, Kimi K3 97.2%, GLM-5.3 91.1%; SimpleQA Verified — Kimi K3 50.6%, GPT-5.6 Terra 43.2%, GLM-5.3 41.0%.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5.6 Terra, GLM-5.3 and Kimi K3 yet, so there is no like-for-like coding score. On overall capability, GPT-5.6 Terra leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
GPT-5.6 Terra and Kimi K3 have the largest context windows (1,050,000 and 1,048,576 tokens), against 1,000,000 for GLM-5.3. Maximum output per response: GPT-5.6 Terra up to 128,000, GLM-5.3 up to 131,072, Kimi K3 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GPT-5.6 Terra accepts text, images and PDFs; GLM-5.3 accepts text; Kimi K3 accepts text, images and video. GPT-5.6 Terra handles the widest range of inputs.
Are any of these open source?
GLM-5.3 and Kimi K3 publishes its weights and can be self-hosted; GPT-5.6 Terra is proprietary.
Which is newer?
GLM-5.3 is the newest, released Aug 14, 2026. Kimi K3 came out Jul 16, 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.
