GLM-4.5V vs Mistral Medium 3.1 vs Relace Apply 3
Too close to call on our weighted score (Mistral Medium 3.1 49, GLM-4.5V 48, Relace Apply 3 32). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-4.5V
48/100- ECI—
- Price$0.60 / $1.80
- Context64K
Mistral AIMistral Medium 3.1
49/100- ECI—
- Price$0.40 / $2.00
- Context131K
Relace
Relace Apply 3
32/100- ECI—
- Price$0.85 / $1.25
- Context256K
Too close to call
It is close. Our weighted score puts them within a point (Mistral Medium 3.1 49/100, GLM-4.5V 48/100, Relace Apply 3 32/100), so choose by what matters most for your work: Mistral Medium 3.1 on price and Relace Apply 3 for long inputs. 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 Medium 3.1Mistral Medium 3.1 $0.80 · GLM-4.5V $0.90 · Relace Apply 3 $0.95 per 1M tokens (3:1 blend)
- Longest contextRelace Apply 3Relace Apply 3 256,000 · Mistral Medium 3.1 131,072 · GLM-4.5V 64,000 tokens
- Widest inputsGLM-4.5V and Mistral Medium 3.1GLM-4.5V: Text, Images, Video · Mistral Medium 3.1: Text, Images, PDFs · Relace Apply 3: Text
- Self-hostingGLM-4.5VPublishes downloadable weights
| Measure | Weight | GLM-4.5V | Mistral Medium 3.1 | Relace Apply 3 |
|---|---|---|---|---|
| Price | 50% | 50 | 52 | 49 |
| Inputs & features | 30% | 70 | 60 | 0 |
| Context window | 20% | 12 | 25 | 36 |
| Overall | 100% | 48/100 | 49/100 | 32/100 |
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-4.5VZ.ai (Zhipu) | Mistral Medium 3.1Mistral AI | Relace Apply 3Relace |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.60 | $0.40 (best) | $0.85 |
| Output | $1.80 | $2.00 | $1.25 (best) |
| Cached input | $0.11 | — | — |
| Blended (3:1) | $0.90 | $0.80 (best) | $0.95 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 4 providers | Median of 3 providers |
| Limits | |||
| Context window | 64,000 tokens | 131,072 tokens | 256,000 tokens (best) |
| Max output | 16,384 tokens | 104,857 tokens | 128,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | No | No |
| Tool calling | Yes | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | glm-4.5v | — | — |
| API providers | 17 (best) | 4 | 3 |
| Released | Aug 11, 2025 | Aug 12, 2025 | Sep 26, 2025 |
| Knowledge cutoff | Apr 2025 | Jun 30, 2025 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GLM-4.5V$9.60
- Mistral Medium 3.1$8.00
- Relace Apply 3$11.00
Which should you choose?
Which is better: GLM-4.5V, Mistral Medium 3.1 or Relace Apply 3?
It is close. Our weighted score puts them within a point (Mistral Medium 3.1 49/100, GLM-4.5V 48/100, Relace Apply 3 32/100), so choose by what matters most for your work: Mistral Medium 3.1 on price and Relace Apply 3 for long inputs. 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-4.5V, Mistral Medium 3.1 or Relace Apply 3?
Mistral Medium 3.1 is cheaper at $0.40 input / $2.00 output per million tokens (median across 4 API providers). GLM-4.5V costs $0.60 input / $1.80 output per million tokens (official Z.AI API price); Relace Apply 3 costs $0.85 input / $1.25 output per million tokens (median across 3 API providers). At a typical mix of three input tokens to one output token, that is $0.80 per million tokens for Mistral Medium 3.1 versus $0.90 for GLM-4.5V (1.1× as much) and $0.95 for Relace Apply 3 (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.5V has not been scored yet, Mistral Medium 3.1 has not been scored yet and Relace Apply 3 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5V, Mistral Medium 3.1 and Relace Apply 3 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Relace Apply 3 does not support tool calling, which most coding agents need.
Which has the bigger context window?
Relace Apply 3 has the largest context window at 256,000 tokens, against 131,072 for Mistral Medium 3.1 and 64,000 for GLM-4.5V. Maximum output per response: GLM-4.5V up to 16,384, Mistral Medium 3.1 up to 104,857, Relace Apply 3 up to 128,000 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5V accepts text, images and video; Mistral Medium 3.1 accepts text, images and PDFs; Relace Apply 3 accepts text. GLM-4.5V handles the widest range of inputs.
Are any of these open source?
GLM-4.5V publishes its weights and can be self-hosted; Mistral Medium 3.1 and Relace Apply 3 is proprietary.
Which is newer?
Relace Apply 3 is the newest, released Sep 26, 2025. Mistral Medium 3.1 came out Aug 12, 2025; GLM-4.5V came out Aug 11, 2025. Knowledge cutoff: GLM-4.5V Apr 2025, Mistral Medium 3.1 Jun 30, 2025.
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.
