AI Models · Open-Source vs Paid

GLM 5.3 Open vs Codestral 2508 (batch) Paid

GLM 5.3 vs Codestral 2508 (batch) compared — price per token, context window, multimodality, openness and which to choose. Can the open-source model replace the paid one? Full 2026 breakdown.

Codestral 2508 (batch) — full profile ›

Prices & specs refreshed from live data · olud.ai

Open-model prices = cheapest provider via OpenRouter; official maker rates may be higher.

GLM 5.3OpenZ.AI

$1.4 /M input
$4.4 /M outputNo per-token fees if you self-host

TypeOpen-weight

Context window1.3M tokens

MultimodalNo

Self-hostYes

Codestral 2508 (batch)PaidMistral AI

$0.15 /M input
$0.45 /M outputManaged API (no infra to run)

TypeProprietary

Context window256K tokens

MultimodalNo

Self-hostNo

Choose GLM 5.3 if you want to self-host, keep your data private and skip per-token fees — it's open-weight and runs on your own hardware. Choose Codestral 2508 (batch) if you want frontier capability through a managed API with zero infrastructure to run.

GLM 5.3 vs Codestral 2508 (batch) specs

Spec	GLM 5.3	Codestral 2508 (batch)	Winner
Maker	Z.AI	Mistral AI	–
Type	Open-weight	Proprietary	GLM 5.3
Context window	1.3M tokens	256K tokens	GLM 5.3
Input price	$1.4/M · free self-host	$0.15/M	Codestral 2508 (batch)
Output price	$4.4/M · free self-host	$0.45/M	Codestral 2508 (batch)
Vision / multimodal	No	No	–
Tool / function calling	Yes	Yes	= Tie
Self-hostable	Yes	No (API only)	GLM 5.3
License	MIT	Proprietary	GLM 5.3

Price gap & when to choose each

9.8×cheaper per output token

Codestral 2508 (batch) is ~9.8× cheaper than GLM 5.3 on output tokens ($0.45 vs $4.4 per M tokens).

Choose GLM 5.3 if…

You want to self-host or run on your cloud
You need the longer 1.3M context window
You prioritize data privacy & control
You are building open or reproducible AI

Choose Codestral 2508 (batch) if…

You want frontier performance through a managed API
You want the lower output price
You value reliability & ecosystem
You don't want to manage infrastructure

Feature comparison

Capability	GLM 5.3	Codestral 2508 (batch)
Open weights (downloadable)	✓	✗
Self-hostable	✓	✗
Runs fully offline	✓	✗
Vision / multimodal	✗	✗
Tool / function calling	✓	✓
1M+ context window	✓	✗

Benchmarks: GLM 5.3 vs Codestral 2508 (batch)

Independent benchmark scores measured by Artificial Analysis. Higher is better (except latency).

GLM 5.3

Codestral 2508 (batch)

Intelligence index

44.9

–

Coding index

74.8

–

GPQA

91.7%

–

Humanity's Last Exam

42.3%

–

Long Context Reasoning

79.7%

–

SciCode

59%

–

τ-Bench Banking

50.3%

–

Terminal-Bench

83.9%

–

Speed

54.8 tok/s

–

Latency

3.57s

–

Intelligence per $

20.9

–

Benchmark data by Artificial Analysis.

How GLM 5.3 and Codestral 2508 (batch) score

🏆 Best value & openness: GLM 5.3 (4.5 vs 3.0 / 5)

Criterion	GLM 5.3	Codestral 2508 (batch)
Cost-efficiency	4.0	5.0
Context window	5.0	4.0
Openness	5.0	1.5
Self-hosting	5.0	1.0
Multimodality	3.5	3.5

Scores come from live data — output price (cost), context length, open vs closed weights (openness & self-hosting) and vision/tool support (multimodality). Raw task quality isn't scored here; it depends on your benchmark — see the verdict.

What each model is

GLM 5.3 Open

Z.AI · Open-weight

GLM-5.3 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks. It supports text input and output with a 1M-token context window, and improves...

Codestral 2508 (batch) Paid

Mistral AI · Proprietary

Mistral's cutting-edge language model for coding released end of July 2025. Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code correction and test generation. [Blog Post](https://mistral.ai/ne

Other models in these families

These variants are tracked but not compared here — one page per family keeps the comparison readable.

Other variants tracked

GLM 5.3 (batch)GLM 5.3 FlashGLM 5.3 Flash (batch)GLM 5.2 (batch)GLM 5.2GLM 5GLM 5 TurboGLM 5.1GLM 5V TurboGLM 4.7GLM 4.6GLM 4.7 FlashGLM 4.5GLM 4.5 AirGLM 4.6VGLM 4.5V

Frequently asked questions

Is GLM 5.3 as good as Codestral 2508 (batch)?

GLM 5.3 is open-weight and competitive on many tasks, but Codestral 2508 (batch) may still lead on the hardest reasoning and agentic work. The gap keeps narrowing — benchmark both on your actual use case before deciding.

Can I run GLM 5.3 locally?

Yes. GLM 5.3 has open weights, so you can self-host it on your own GPUs or run it via a low-cost API. Codestral 2508 (batch) is API-only and cannot be self-hosted.

GLM 5.3 vs Codestral 2508 (batch) — which should I pick in 2026?

Choose GLM 5.3 if you want to self-host, keep your data private and skip per-token fees — it's open-weight and runs on your own hardware. Choose Codestral 2508 (batch) if you want frontier capability through a managed API with zero infrastructure to run.

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