Pairwise benchmark snapshot · Aug 16, 2026noindex (thin pair)

GLM-5.3 vs Mistral: Mixtral 8x22B Instruct benchmark

Name: GLM-5.3 vs Mistral: Mixtral 8x22B Instruct AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares GLM-5.3 (Zhipu) and Mistral: Mixtral 8x22B Instruct (Mistral) using the latest snapshots we have as of Aug 16, 2026. GLM-5.3 wins on output price at $1.8/1M. GLM-5.3 has the larger context window (200k tokens). Every cell below is a dated snapshot from a named public source — we do not invent missing scores, and we do not run SWE-bench ourselves.

Share Analysis:

WhatsApp Telegram X / Post LinkedIn Reddit

Zhipu

GLM-5.3

Z.ai Aug 14 2026 post-train of the GLM-5.2 744B base. Coding-plan live; open weights promised after a two-week safety review (z.ai/blog/glm-5.3).

Elo 1,558$1.8/1M out

Mistral

Mistral: Mixtral 8x22B Instruct

Auto-discovered from OpenRouter (mistralai/mixtral-8x22b-instruct). Preview until a second source matches.

$6/1M out

Swap or Add Any Model to this Matchup

Instant pairwise benchmark recalculation across 400+ LLMs

Model A

Model B

Quick vs:vs Claude Opus 4.5 vs Claude Opus 4.6 vs Claude Sonnet 4.5 vs Claude 3.7 Sonnet

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

GLM-5.3 (10)vsMistral: Mixtral 8x22B Instruct (0)

GLM-5.3: 10W (100%)Overall: GLM-5.3Mistral: Mixtral 8x22B Instruct: 0W (0%)

← GLM-5.3Mistral: Mixtral 8x22B Instruct →

GLM-5.3 (5/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

1,558vs—

Coding Elo

1,586vs—

SWE-bench

76.4%vs—

LiveBench

70.8%vs—

GPQA Diamond

83.2%vs—

GLM-5.3: 5WMistral: Mixtral 8x22B Instruct: 0W

GLM-5.3 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

90 tok/svs—

Time to first token

255 msvs—

GLM-5.3: 2WMistral: Mixtral 8x22B Instruct: 0W

GLM-5.3 (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$1.8/1Mvs$6/1M

Input price

$0.5/1Mvs$2/1M

Context window

200kvs66k

GLM-5.3: 3WMistral: Mixtral 8x22B Instruct: 0W

Capability Matchup Radar

Multi-Dimensional Capability Radar

Each spoke is a skill. Farther from the center is better (0–100th percentile). Tap any dot to inspect details.

Tap any node to inspect

Percentile 0–100

Dimensional AdvantageGLM-5.3 6/6

Preference Elo

No shared data

Throughput Speed

No shared data

Price Efficiency

GLM-5.3

$4.20/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,558

EloHigh is better

—

Code Elo

High is better

1,586

Code EloHigh is better

—

LiveBench

High is better

70.8%

LiveBenchHigh is better

—

SWE-bench

High is better

76.4%

SWE-benchHigh is better

—

GPQA

High is better

83.2%

GPQAHigh is better

—

TTFT

Low is better

255 ms

TTFTLow is better

—

Speed

High is better

90 tok/s

SpeedHigh is better

—

In $

Low is better

+300%$0.5/1M

In $Low is better

$2/1M

Out $

Low is better

+233%$1.8/1M

Out $Low is better

$6/1M

Context

High is better

+205%200k

ContextHigh is better

66k

Interactive Simulator

Workload Cost & Savings Calculator

Simulate monthly production API costs based on published $/1M tokens.

Save up to 72% with GLM-5.3

Monthly Volume50M tokens/mo

Quick Presets:

Token Ratio (In vs Out)70% In / 30% Out

RAG / Search (10% out)Coding / Chat (50% out)

GLM-5.3$44.50 / mo

In: $17.50Out: $27.00

Mistral: Mixtral 8x22B Instruct$160.00 / mo

In: $70.00Out: $90.00

Estimated Cost Delta

GLM-5.3 is estimated to save $115.50/month ($1,386/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
GLM-5.3:Pick GLM-5.3 when you are optimizing output cost.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatGLM-5.3

Lower output list price ($1.8/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGGLM-5.3

Larger window (200k).

Screenshots / visionGLM-5.3

GLM-5.3 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	GLM-5.3	Lower output list price ($1.8/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	GLM-5.3	Larger window (200k).
Screenshots / vision	GLM-5.3	GLM-5.3 is the side marked multimodal in the catalog.

Related Editorial Dispatches & Benchmark Notes

View all news

launchAug 14, 2026

GLM-5.3 (Aug 14): Z.ai post-train of the 5.2 744B base

Coding-plan live; open weights promised after a two-week safety review. Newest Zhipu row.

launchJun 18, 2026

GLM-5.2 remains the prior Zhipu flagship for upgrade pairs

Strong Chinese/English coding. 5.3 is the new row; 5.2 stays so the delta is visible.

Community Sentiment

Cast Your Matchup Vote

0 total votes

Community Discussions (0)

No comments posted on this matchup yet. Be the first to share an evaluation note!

Share card

The real PNG is generated at /compare/glm-5-3-vs-mixtral-8x22b-instruct/opengraph-image for crawlers.

PNG

CompareLLM Matrix

Verified head-to-head card

LIVE

Zhipu

GLM-5.3

Elo 1,558

LiveBench70.8%

SWE-bench76.4%

Speed90 tok/s

Output cost$1.8/1M

Mistral

Mistral: Mixtral 8x22B Instruct

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$6/1M

Frequently Asked Questions

Which is better overall, GLM-5.3 or Mistral: Mixtral 8x22B Instruct?: We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.
Which is better at coding, GLM-5.3 or Mistral: Mixtral 8x22B Instruct?: SWE-bench is missing for at least one model, so we do not rank coding from a single number.
Which is cheaper to run in production?: GLM-5.3 output tokens are $1.8/1M versus $6/1M. Input prices and retry rates still move the real bill.
Which is faster, GLM-5.3 or Mistral: Mixtral 8x22B Instruct?: We do not have TTFT for both models.
Which has the larger context window?: GLM-5.3 accepts 200k tokens versus 66k.
Can I self-host either model?: Neither model is marked open-weights here. You are comparing hosted APIs.
Are these scores from CompareLLM’s own evals?: No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.
What does preference Elo mean on this page?: Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. GLM-5.3 and Mistral: Mixtral 8x22B Instruct Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All GLM-5.3 matchups →|All Mistral: Mixtral 8x22B Instruct matchups →

GLM-5.3 vs GLM-5.2 (previous glm)Mistral Large 3 vs GLM-5.3 GPT-5 vs GLM-5.3 Claude Sonnet 5 vs GLM-5.3 Gemini 3 Pro vs GLM-5.3 Claude Opus 4.5 vs GLM-5.3

GLM-5.3 vs Mistral: Mixtral 8x22B Instruct benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

GLM-5.3 (10)vsMistral: Mixtral 8x22B Instruct (0)

GLM-5.3: 10W (100%)Overall: GLM-5.3Mistral: Mixtral 8x22B Instruct: 0W (0%)

← GLM-5.3Mistral: Mixtral 8x22B Instruct →

GLM-5.3 (5/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

1,558vs—

Coding Elo

1,586vs—

SWE-bench

76.4%vs—

LiveBench

70.8%vs—

GPQA Diamond

83.2%vs—

GLM-5.3: 5WMistral: Mixtral 8x22B Instruct: 0W

GLM-5.3 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

90 tok/svs—

Time to first token

255 msvs—

GLM-5.3: 2WMistral: Mixtral 8x22B Instruct: 0W

GLM-5.3 (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$1.8/1Mvs$6/1M

Input price

$0.5/1Mvs$2/1M

Context window

200kvs66k

GLM-5.3: 3WMistral: Mixtral 8x22B Instruct: 0W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatGLM-5.3

Lower output list price ($1.8/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGGLM-5.3

Larger window (200k).

Screenshots / visionGLM-5.3

GLM-5.3 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	GLM-5.3	Lower output list price ($1.8/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	GLM-5.3	Larger window (200k).
Screenshots / vision	GLM-5.3	GLM-5.3 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, GLM-5.3 or Mistral: Mixtral 8x22B Instruct?

We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.

Which is better at coding, GLM-5.3 or Mistral: Mixtral 8x22B Instruct?

SWE-bench is missing for at least one model, so we do not rank coding from a single number.

Which is cheaper to run in production?

GLM-5.3 output tokens are $1.8/1M versus $6/1M. Input prices and retry rates still move the real bill.

Which is faster, GLM-5.3 or Mistral: Mixtral 8x22B Instruct?

We do not have TTFT for both models.

Which has the larger context window?

GLM-5.3 accepts 200k tokens versus 66k.

Can I self-host either model?

Neither model is marked open-weights here. You are comparing hosted APIs.

Are these scores from CompareLLM’s own evals?

No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.

What does preference Elo mean on this page?

Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. GLM-5.3 and Mistral: Mixtral 8x22B Instruct Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

No shared data

No shared data

GLM-5.3

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

GLM-5.3 (Aug 14): Z.ai post-train of the 5.2 744B base

GLM-5.2 remains the prior Zhipu flagship for upgrade pairs

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

GLM-5.3

Mistral: Mixtral 8x22B Instruct

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

No shared data

No shared data

GLM-5.3

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

GLM-5.3 (Aug 14): Z.ai post-train of the 5.2 744B base

GLM-5.2 remains the prior Zhipu flagship for upgrade pairs

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

GLM-5.3

Mistral: Mixtral 8x22B Instruct

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons