ZMIME
Comparison · 3 models · Updated Nov 2, 2026

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.

  1. Mistral AI

    Codestral 25.08

    Released Jul 30, 2025

    47/100
    • ECI—
    • Price$0.30 / $0.90
    • Context128K
  2. Baidu

    ERNIE 4.5 VL 424B A47B

    Released Jun 30, 2025

    44/100
    • ECI—
    • Price$0.42 / $1.25
    • Context123K
  3. Z.ai (Zhipu)

    GLM-4.5-Air

    Released Jul 28, 2025

    48/100
    • ECI—
    • Price$0.20 / $1.10
    • Context131K
01 — Verdict

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
How the score is built
MeasureWeightCodestral 25.08ERNIE 4.5 VL 424B A47B GLM-4.5-Air
Price50%645765
Inputs & features30%353535
Context window20%242325
Overall100%47/10044/10048/100

Left out because at least one model lacks the data: capability. The remaining weights were rescaled.

02 — Side by side

Every spec in one table

Highlighted cells lead their row. Dashes mean the data is not published.

Codestral 25.08 vs ERNIE 4.5 VL 424B A47B vs GLM-4.5-Air specifications side by side
SpecificationCodestral 25.08Mistral AIERNIE 4.5 VL 424B A47B BaiduGLM-4.5-AirZ.ai (Zhipu)
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 rateSame rateSame rateSame rate
Price sourceOfficial Mistral APIMedian of 1 providersOfficial Z.AI API
Limits
Context window128,000 tokens123,000 tokens131,072 tokens (best)
Max output8,192 tokens16,000 tokens98,304 tokens (best)
Inputs and features
TextYesYesYes
ImagesNoYesNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningNoYesYes
Tool callingYesNoYes
Structured outputYesNoNo
Availability
WeightsProprietaryOpenOpen
API model IDcodestral-2508—glm-4.5-air
API providers11119 (best)
ReleasedJul 30, 2025Jun 30, 2025Jul 28, 2025
Knowledge cutoff—Mar 31, 2025Apr 2025
03 — Cost

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
04 — Questions

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.