GLM-4.6 vs Mistral Large 3 vs Ling-1T
Mistral Large 3 comes out ahead, 50 to 42 and 37 on our weighted score, and it is the cheaper option too.
Z.ai (Zhipu)GLM-4.6
42/100- ECI—
- Price$0.60 / $2.20
- Context205K
- Our pick
Mistral AIMistral Large 3
50/100- ECI—
- Price$0.50 / $1.50
- Context262K
inclusionAILing-1T
37/100- ECI—
- Price$0.57 / $2.29
- Context128K
Mistral Large 3 is our pick
Mistral Large 3 is the better all-round choice, scoring 50/100 against GLM-4.6 (42) and Ling-1T (37). It leads on price, inputs & features and context window. 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 priceMistral Large 3Mistral Large 3 $0.75 · GLM-4.6 $1.00 · Ling-1T $1.00 per 1M tokens (3:1 blend)
- Longest contextMistral Large 3Mistral Large 3 262,144 · GLM-4.6 204,800 · Ling-1T 128,000 tokens
- Widest inputsMistral Large 3GLM-4.6: Text · Mistral Large 3: Text, Images · Ling-1T: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.6 | Mistral Large 3 | Ling-1T |
|---|---|---|---|---|
| Price | 50% | 50 | 56 | 50 |
| Inputs & features | 30% | 35 | 50 | 25 |
| Context window | 20% | 32 | 37 | 24 |
| Overall | 100% | 42/100 | 50/100 | 37/100 |
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 | GLM-4.6Z.ai (Zhipu) | Mistral Large 3Mistral AI | Ling-1TinclusionAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.60 | $0.50 (best) | $0.57 |
| Output | $2.20 | $1.50 (best) | $2.29 |
| Cached input | $0.11 | $0.05 (best) | — |
| Blended (3:1) | $1.00 | $0.75 (best) | $1.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Mistral API | Official Bailing API |
| Limits | |||
| Context window | 204,800 tokens | 262,144 tokens (best) | 128,000 tokens |
| Max output | 131,072 tokens | 262,144 tokens (best) | 32,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-4.6 | mistral-large-2512 | Ling-1T |
| API providers | 18 (best) | 13 | 1 |
| Released | Sep 30, 2025 | Dec 2, 2025 | Oct 2025 |
| Knowledge cutoff | Apr 2025 | Nov 2024 | Jun 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GLM-4.6$10.40
- Mistral Large 3$8.00
- Ling-1T$10.28
Which should you choose?
Which is better: GLM-4.6, Mistral Large 3 or Ling-1T?
Mistral Large 3 is the better all-round choice, scoring 50/100 against GLM-4.6 (42) and Ling-1T (37). It leads on price, inputs & features and context window. 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, GLM-4.6, Mistral Large 3 or Ling-1T?
Mistral Large 3 is cheaper at $0.50 input / $1.50 output per million tokens (official Mistral API price). GLM-4.6 costs $0.60 input / $2.20 output per million tokens (official Z.AI API price); Ling-1T costs $0.57 input / $2.29 output per million tokens (official Bailing API price). At a typical mix of three input tokens to one output token, that is $0.75 per million tokens for Mistral Large 3 versus $1.00 for GLM-4.6 (1.3× as much) and $1.00 for Ling-1T (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.6 has not been scored yet, Mistral Large 3 has not been scored yet and Ling-1T has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.6, Mistral Large 3 and Ling-1T 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?
Mistral Large 3 has the largest context window at 262,144 tokens, against 204,800 for GLM-4.6 and 128,000 for Ling-1T. Maximum output per response: GLM-4.6 up to 131,072, Mistral Large 3 up to 262,144, Ling-1T up to 32,000 tokens.
Which can read images, PDFs, audio or video?
GLM-4.6 accepts text; Mistral Large 3 accepts text and images; Ling-1T accepts text. Mistral Large 3 handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Mistral Large 3 is the newest, released Dec 2, 2025. Ling-1T came out Oct 2025; GLM-4.6 came out Sep 30, 2025. Knowledge cutoff: GLM-4.6 Apr 2025, Mistral Large 3 Nov 2024, Ling-1T Jun 2024.
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 Oct 4, 2026.
