Model comparison
Compare Mistral Medium 3.5 and GLM-5.3 using the same provider-sourced text & reasoning rubric. No mystery score and no invented benchmark ranking.
Facts checked September 4, 2026
Set your usage. Your estimate updates as you type.
Example: 6,000 input tokens for 10 pages, plus a 500-token summary. Page lengths vary; adjust the numbers below.
One run sends your input to the model once and receives an answer. Tokens are pieces of text: input is what you send, output is the answer you receive.
Estimates exclude taxes, tools, cache storage/writes, free allowances and custom discounts. Image estimates cover output only, not prompt or reference-image charges. Quality modes differ by model. Unlisted settings are not treated as free.
| Model | Access | Estimated total (USD) |
|---|---|---|
| GLM-5.3Z.ai | Z.ai | No reviewed rate |
| Mistral Medium 3.5Mistral AI | Mistral AI | No reviewed rate |
Quick take
Mistral's open-weight frontier model for demanding multimodal, coding, reasoning, and agent workflows, with a 256K context window.
The headline token price applies to Mistral's standard hosted endpoint. Regional inference costs more, and self-hosting shifts the bill to GPUs, operations, and support.
Z.ai's open-weight long-context reasoning model for coding, tools, and agentic work across self-hosted and managed routes.
Compare the exact checkpoint and host, not just the model name. Context, maximum output, speed, price, logging, and reasoning controls can all change by route.
Compare the published facts
Values use each provider's own published units and limits. A blank means the provider did not publish a directly comparable value in the sources reviewed.
| Text & reasoning | Mistral Medium 3.5 | GLM-5.3 |
|---|---|---|
| Context windowMaximum combined prompt and working context documented by the provider. | 256K tokens combined | Up to 1.31M on OpenRouter; 1M on QwenCloud |
| Maximum outputProvider-published response limit, where available. | Not separately published | Up to 262,144 tokens on OpenRouter |
| Knowledge cutoffLatest reliable knowledge date explicitly published by the model provider. Search and connected tools can retrieve newer information but do not change the model's built-in cutoff. | Not published | Not published |
| Input priceCurrent standard list price per million input tokens unless noted. | $1.50 / 1M | From $1.15 / 1M on OpenRouter |
| Output priceCurrent standard list price per million output tokens unless noted. | $7.50 / 1M | From $3.50 / 1M on OpenRouter |
| InputsMedia types accepted by the listed model endpoint. | Text, image | Text |
| Tools & agentsSelected native tools and agent-building capabilities, not an exhaustive list. | Functions, agents, built-in tools, structured and predicted outputs | Tools, structured outputs, controllable reasoning on supported routes |
How to choose
Start with the job you need to complete, then validate cost, access, and policy details on your exact provider route.
Mistral's commercial terms exclude model training by default except when a customer opts in or uses designated Labs or preview models. Standard API input and output retention is generally 30 rolling days for abuse monitoring unless zero data retention is enabled.
Provider and API links
Self-hosted weights keep prompts inside infrastructure you control. QwenCloud says it does not retain API inputs or outputs for training; OpenRouter and other providers apply their own route policies.
Frequently asked questions
Mistral Medium 3.5: Mistral's open-weight frontier model for demanding multimodal, coding, reasoning, and agent workflows, with a 256K context window. GLM-5.3: Z.ai's open-weight long-context reasoning model for coding, tools, and agentic work across self-hosted and managed routes.
Consider Mistral Medium 3.5 when your priority is Complex coding and agent workflows. Consider GLM-5.3 when your priority is Long-context coding agents. Test both with your own data and provider route before committing.
No. This comparison aligns provider-published facts for the Text & reasoning category. It does not claim a universal winner or combine incompatible third-party benchmark scores.
