Model comparison

GLM 5.3 vs Mistral Large 3

Z.ai against Mistral, compared on context, price, and attributed benchmark results.

Recorded profile verification October 2, 2026Individual provider fields may changeEvidence confidence: Medium — see receipts below

Z.ai

GLM 5.3

Frontier · Open weights

Mistral

Mistral Large 3

Frontier · Open weights

AI model capability comparison
Specification	GLM 5.3	Mistral Large 3
Provider
Provider	Z.ai	Mistral
Tier
Tier	Frontier	Frontier
Context window
Context window	Winner: 1M	262K
Max output
Max output	131K	Not verifiedUnverified
Input / 1M tokens
Input / 1M tokens	$1.40	Winner: $0.50
Output / 1M tokens
Output / 1M tokens	$4.40	Winner: $1.50
Weights
Weights	Open	Open
Parameters
Parameters	753B total (MoE; active count not verified)	675B total / 41B active (sparse MoE)
Reasoning levels
Reasoning levels	low, high, max	Not verifiedUnverified
Modalities
Modalities	text	text, image
License
License	GLM-5.3 License (custom)	Apache 2.0
API model id
API model id	glm-5.3	mistral-large-2512
Released
Released	August 14, 2026	December 2, 2025
Artificial Analysis Intelligence Index [max] · source date GLM 5.3: 2026-10-01
Artificial Analysis Intelligence Index [max] · source date GLM 5.3: 2026-10-01	45	Not verifiedUnverified
Artificial Analysis Intelligence Index · source date Mistral Large 3: 2026-10-01
Artificial Analysis Intelligence Index · source date Mistral Large 3: 2026-10-01	Not verifiedUnverified	9
Terminal-Bench 2.1 · source date GLM 5.3: 2026-08-14
Terminal-Bench 2.1 · source date GLM 5.3: 2026-08-14	88.2	Not verifiedUnverified
DeepSWE 1.1 · source date GLM 5.3: 2026-08-14
DeepSWE 1.1 · source date GLM 5.3: 2026-08-14	66.9	Not verifiedUnverified
Humanity's Last Exam · source date GLM 5.3: 2026-08-14
Humanity's Last Exam · source date GLM 5.3: 2026-08-14	62.5	Not verifiedUnverified
GPQA Diamond · source date Mistral Large 3: 2026-09-26
GPQA Diamond · source date Mistral Large 3: 2026-09-26	Not verifiedUnverified	68

Dates beside benchmark scores refer to the linked source’s publication or capture date unless that source explicitly states when it measured the result. A shared URL alone does not establish comparable runs; unverified setups are marked. Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.

Benchmark receipts

Where each score comes from, and how far it can be compared across models.

Source date2026-10-01
Artificial Analysis (Mistral Large 3)
Composite index blending reasoning, knowledge, and coding evals into one 0–100 score.
Comparable with caveatOnly same-version scores are comparable. v4.3.2 was rebaselined (the top score fell from 66 on v4.1.1 to ~58), so v4.1.1 figures must not be compared with v4.3.2 figures — check the cited source and its date.
Source date2026-10-01
Artificial Analysis (GLM-5.3 (max))
Composite index blending reasoning, knowledge, and coding evals into one 0–100 score.
Comparable with caveatOnly same-version scores are comparable. v4.3.2 was rebaselined (the top score fell from 66 on v4.1.1 to ~58), so v4.1.1 figures must not be compared with v4.3.2 figures — check the cited source and its date.
Source date2026-09-26
Artificial Analysis GPQA Diamond (independent; replaces Mistral's non-comparable base-model 5-shot 43.9)
Graduate-level science reasoning: multiple-choice questions written by domain PhDs.
Comparable with caveatDo not treat a fixed two-point band as universal measurement uncertainty; it depends on run count and evaluation setup. Chain-of-thought, prompt, and tool policy must match. The small pool limits fine-grained conclusions, and frontier scores can be near ceiling; Artificial Analysis dropped it from its Intelligence Index in v4.3.
Source date2026-08-14
Z.ai GLM-5.3 blog + HF model card (vendor, with tools, full set)
Frontier-knowledge ceiling: extremely hard multi-domain questions written to be near-impossible without deep expertise.
Comparable with caveatSubset/tool configurations (text-only vs with-tools) differ between vendors and materially change scores; must match configuration to compare.
Source date2026-08-14
Z.ai GLM-5.3 blog + HF model card (vendor, mini-swe-agent family)
Agentic software engineering: pass@1 on real repo tasks with reported cost per completed task.
Directly comparable
Source date2026-08-14
Z.ai GLM-5.3 blog + HF model card eval results (vendor, Claude Code harness)
Agentic terminal work: multi-step tasks executed in a sandboxed shell environment.
Comparable with caveatNot comparable with Terminal-Bench 3.0 or 4.0 (different task sets) or v1; harness configuration (container, time limits) also shifts results.

Pricing tiers

GLM 5.3: Z.ai token API $1.40/$4.40 per MTok; cached input $0.26. Cache storage is listed as limited-time free, with no verified end date. Coding Plan points are separate: off-peak/weekend calls consume half the standard points. API docs advertise 1M context and specify a 131,072 output ceiling.

Mistral Large 3: Mistral first-party API $0.50/$1.50 per MTok; cached input $0.05. The 675B-total / 41B-active multimodal checkpoint declares Apache 2.0. Regional hosting is endpoint-specific.

FrontierOpen weightsRecord checked October 2, 2026

GLM 5.3

Z.ai's text-only GLM flagship, with a 753B-total MoE checkpoint and always-enabled reasoning. Published benchmark figures identify particular harnesses and settings.

Best for

Coding Plan subscribers
Long-horizon coding
Chinese + English

Watch out

Custom licence: model-as-a-service operators whose licensee/affiliate aggregate revenue exceeds $10B in any consecutive 12 months require Z.AI security review before commercial use; the definition excludes embedded end-user features/harnesses and mere relays. Keep attribution/permission notices. Reasoning defaults to max; disabling it fails. Accounts that ever subscribed to Coding Plan, including expired subscriptions, currently have Model API access only through the Chat Completions-compatible protocol. Active parameter count and exact initial weight-publication day remain unverified.

FrontierOpen weightsRecord checked October 2, 2026

Mistral Large 3

Mistral's December 2025 multimodal 675B/41B sparse MoE with a 256K context; Medium 3.5 is a separate dense model with configurable reasoning.

Best for

Open-weight deployments
Multimodal EU-hosted inference
Balanced frontier work

Watch out

The publisher describes Large 3 as not a dedicated reasoning model and documents vision limitations. It advertises FP8 deployment on a B200/H200 node or a separate NVFP4 checkpoint on H100/A100 nodes; this is not a guarantee for every serving setup. A separate hard output cap is not verified. Confirm regional residency on the actual endpoint.

When the cheaper one wins

Mistral Large 3 is cheaper on output at $1.50 per million tokens against $4.40 for GLM 5.3 — about 2.9×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. This compares standard short-context token rates, not total cost per accepted task. Include input, cache, context tiers, schedules, retries and review in your pilot.

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Evidence confidence: Medium

How strong and complete the evidence behind this comparison is — not a prediction of which model is better.

Pricing verified on both sides — Input and output rates are verified for both models.
3/5 core specs verified on both sides — Not published for at least one side: max output, reasoning levels.
No directly aligned benchmark setup — No benchmark has matching cited sources, source dates, units, effort and recorded run details on both models.
Verified within the last 90 days — Older of the two catalog checks was 24 days ago.
Both models carry source citations — Each side has at least two catalog sources on record.

Source receipts

Each catalog figure was checked against the provider or an independent second source on the date shown.

GLM 5.3: Z.ai GLM-5.3 announcement (accessed 2026-10-01)
GLM 5.3: GLM-5.3 official API limits and account/protocol restrictions (accessed 2026-10-01)
GLM 5.3: Z.ai exact output ceiling (accessed 2026-10-01)
GLM 5.3: Z.ai current first-party prices (accessed 2026-10-01)
GLM 5.3: GLM-5.3 checkpoint and publisher benchmarks (accessed 2026-10-01)
GLM 5.3: GLM-5.3 exact custom licence (accessed 2026-10-01)
Mistral Large 3: Current Mistral Large 3 API card (GA) (accessed 2026-10-01)
Mistral Large 3: Mistral pricing and exact cache rate (accessed 2026-10-01)
Mistral Large 3: Mistral Large 3 announcement (accessed 2026-10-01)
Mistral Large 3: Mistral changelog: identifier and release (accessed 2026-10-01)
Mistral Large 3: Large 3 official instruct checkpoint and Apache declaration (accessed 2026-10-01)

Related comparisons

Diving deeper on one model? GLM 5.3 · Mistral Large 3

Common questions

GLM 5.3 vs Mistral Large 3

Answered from the verified figures on this page rather than general guidance.

Is GLM 5.3 or Mistral Large 3 cheaper for input?

Mistral Large 3 is cheaper at $0.50 per million input tokens, against $1.40 for GLM 5.3 — roughly 2.8× the price. Estimate both input and output token volume for your workload. These are base rates; GLM 5.3: Z.ai token API $1.40/$4.40 per MTok; cached input $0.26. Cache storage is listed as limited-time free, with no verified end date. Coding Plan points are separate: off-peak/weekend calls consume half the standard points. API docs advertise 1M context and specify a 131,072 output ceiling. Mistral Large 3: Mistral first-party API $0.50/$1.50 per MTok; cached input $0.05. The 675B-total / 41B-active multimodal checkpoint declares Apache 2.0. Regional hosting is endpoint-specific.

Is GLM 5.3 or Mistral Large 3 cheaper for output?

Mistral Large 3 is cheaper at $1.50 per million output tokens, against $4.40 for GLM 5.3 — roughly 2.9× the price. Estimate both input and output token volume for your workload. These are base rates; GLM 5.3: Z.ai token API $1.40/$4.40 per MTok; cached input $0.26. Cache storage is listed as limited-time free, with no verified end date. Coding Plan points are separate: off-peak/weekend calls consume half the standard points. API docs advertise 1M context and specify a 131,072 output ceiling. Mistral Large 3: Mistral first-party API $0.50/$1.50 per MTok; cached input $0.05. The 675B-total / 41B-active multimodal checkpoint declares Apache 2.0. Regional hosting is endpoint-specific.

Which has the larger context window, GLM 5.3 or Mistral Large 3?

GLM 5.3 accepts 1M tokens against 262K for Mistral Large 3. This only matters if you routinely send very long documents or large codebases.

Should I use GLM 5.3 or Mistral Large 3?

Both are classified in our frontier tier; this editorial grouping does not imply equal capability. GLM 5.3 is a candidate for coding plan subscribers; Mistral Large 3 is a candidate for open-weight deployments. Compare accuracy, tool reliability, latency and cost on representative tasks.

Next step

Choosing between them

The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.

Input price: Mistral Large 3 costs $0.50 per 1M tokens versus $1.40 for GLM 5.3 — a 2.8x difference at the headline tier.
Context: GLM 5.3 takes 1M against 262K for Mistral Large 3 — only decisive if your prompts approach the smaller window.

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