Codestral 25.08 vs ERNIE 4.5 VL 424B A47B vs GLM-4.5-Air
Too close to call on our weighted score (GLM-4.5-Air 48, Codestral 25.08 47, ERNIE 4.5 VL 424B A47B 44). The right pick depends on what you value most.
Mistral AI
Codestral 25.08
47/100- ECI—
- Price$0.30 / $0.90
- Context128K
Baidu
ERNIE 4.5 VL 424B A47B
44/100- ECI—
- Price$0.42 / $1.25
- Context123K
Z.ai (Zhipu)
GLM-4.5-Air
48/100- ECI—
- Price$0.20 / $1.10
- Context131K
Too close to call
It is close. Our weighted score puts them within a point (GLM-4.5-Air 48/100, Codestral 25.08 47/100, ERNIE 4.5 VL 424B A47B 44/100), so choose by what matters most for your work: GLM-4.5-Air on price and GLM-4.5-Air 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 priceGLM-4.5-AirGLM-4.5-Air $0.425 · Codestral 25.08 $0.45 · ERNIE 4.5 VL 424B A47B $0.627 per 1M tokens (3:1 blend)
- Longest contextGLM-4.5-AirGLM-4.5-Air 131,072 · Codestral 25.08 128,000 · ERNIE 4.5 VL 424B A47B 123,000 tokens
- Widest inputsERNIE 4.5 VL 424B A47B Codestral 25.08: Text · ERNIE 4.5 VL 424B A47B : Text, Images · GLM-4.5-Air: Text
- Self-hostingERNIE 4.5 VL 424B A47B and GLM-4.5-AirPublishes downloadable weights
| Measure | Weight | Codestral 25.08 | ERNIE 4.5 VL 424B A47B | GLM-4.5-Air |
|---|---|---|---|---|
| Price | 50% | 64 | 57 | 65 |
| Inputs & features | 30% | 35 | 35 | 35 |
| Context window | 20% | 24 | 23 | 25 |
| Overall | 100% | 47/100 | 44/100 | 48/100 |
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 | |||
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.30 | $0.42 | $0.20 (best) |
| Output | $0.90 (best) | $1.25 | $1.10 |
| Cached input | $0.03 | — | $0.03 |
| Blended (3:1) | $0.45 | $0.627 | $0.425 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Mistral API | Median of 1 providers | Official Z.AI API |
| Limits | |||
| Context window | 128,000 tokens | 123,000 tokens | 131,072 tokens (best) |
| Max output | 8,192 tokens | 16,000 tokens | 98,304 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | Yes | Yes |
| Tool calling | Yes | No | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | codestral-2508 | — | glm-4.5-air |
| API providers | 11 | 1 | 19 (best) |
| Released | Jul 30, 2025 | Jun 30, 2025 | Jul 28, 2025 |
| Knowledge cutoff | — | Mar 31, 2025 | Apr 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
Codestral 25.08$4.80
ERNIE 4.5 VL 424B A47B $6.70
GLM-4.5-Air$4.20
Which should you choose?
Which is better: Codestral 25.08, ERNIE 4.5 VL 424B A47B or GLM-4.5-Air?
It is close. Our weighted score puts them within a point (GLM-4.5-Air 48/100, Codestral 25.08 47/100, ERNIE 4.5 VL 424B A47B 44/100), so choose by what matters most for your work: GLM-4.5-Air on price and GLM-4.5-Air 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, Codestral 25.08, ERNIE 4.5 VL 424B A47B or GLM-4.5-Air?
GLM-4.5-Air is cheaper at $0.20 input / $1.10 output per million tokens (official Z.AI API price). Codestral 25.08 costs $0.30 input / $0.90 output per million tokens (official Mistral API price); ERNIE 4.5 VL 424B A47B costs $0.42 input / $1.25 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.425 per million tokens for GLM-4.5-Air versus $0.45 for Codestral 25.08 (1.1× as much) and $0.627 for ERNIE 4.5 VL 424B A47B (1.5× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Codestral 25.08 has not been scored yet, ERNIE 4.5 VL 424B A47B has not been scored yet and GLM-4.5-Air has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Codestral 25.08, ERNIE 4.5 VL 424B A47B and GLM-4.5-Air yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that ERNIE 4.5 VL 424B A47B does not support tool calling, which most coding agents need.
Which has the bigger context window?
GLM-4.5-Air has the largest context window at 131,072 tokens, against 128,000 for Codestral 25.08 and 123,000 for ERNIE 4.5 VL 424B A47B . Maximum output per response: Codestral 25.08 up to 8,192, ERNIE 4.5 VL 424B A47B up to 16,000, GLM-4.5-Air up to 98,304 tokens.
Which can read images, PDFs, audio or video?
Codestral 25.08 accepts text; ERNIE 4.5 VL 424B A47B accepts text and images; GLM-4.5-Air accepts text. ERNIE 4.5 VL 424B A47B handles the widest range of inputs.
Are any of these open source?
ERNIE 4.5 VL 424B A47B and GLM-4.5-Air publishes its weights and can be self-hosted; Codestral 25.08 is proprietary.
Which is newer?
Codestral 25.08 is the newest, released Jul 30, 2025. GLM-4.5-Air came out Jul 28, 2025; ERNIE 4.5 VL 424B A47B came out Jun 30, 2025. Knowledge cutoff: ERNIE 4.5 VL 424B A47B Mar 31, 2025, GLM-4.5-Air Apr 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.