GLM 5.1 vs GLM 5.2

At a Glance

Compare
Pricing and Limits
Input priceFrom · USD / 1M tokens$1.05Deepinfra ↗ · Sep 24, 2026$0.75Deepinfra ↗ · Oct 19, 2026
Output priceFrom · USD / 1M tokens$3.50Deepinfra ↗ · Sep 24, 2026$2.40Deepinfra ↗ · Oct 19, 2026
Context windowMaximum documented tokens203K1,049K
Model facts checkedAug 28, 2026View model evidence →Aug 28, 2026View model evidence →

Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →

Available Benchmarks

All benchmark results →
BenchmarkGLM-5.1GLM-5.2
LMArena Text Arenatext-2026-10-15-0ebb411abf00 · arena_rating · leader1,462.4099% of row best · rating · glm-5.1; 95% CI [1458.83486169, 1465.97294166]; votes 62889; rank 391,470.37100% of row best · rating · glm-5.2-max; 95% CI [1466.22224854, 1474.51957977]; votes 49820; rank 31
Overall ResultCounted from the protocol-matched rows above0 benchmark winsNo overall winner1 benchmark winNo overall winner

Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.

Side-by-Side Facts

FieldGLM-5.1GLM-5.2
DeveloperZ.aiZ.ai
FamilyGlm 5 1Glm 5 2
ModelGLM-5.1GLM-5.2
VersionGLM-5.1GLM-5.2
Lifecycleactiveactive
ReleasedUnknownUnknown
Knowledge cutoffUnknownUnknown
Input modalitiesTextText
Output modalitiesTextText
Context window203K1,049K
Total parameters753.9B753.3B
Active parametersUnknownUnknown
Licensemitmit
Open weightsYesYes
API availableYesYes
Self-hostableYesYes
Provider accessDeepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard)Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard)
Capabilitieschat, generation, reasoning, toolschat, generation, reasoning, tools

GLM 5.1 Capabilities

chatgenerationreasoningtools
Serving providers4
Canonical IDzai-org/GLM-5.1

GLM 5.2 Capabilities

chatgenerationreasoningtools
Serving providers5
Canonical IDzai-org/GLM-5.2

Primary Evidence

Sources and Freshness

Questions

GLM 5.1 vs GLM 5.2 FAQs

Is GLM 5.1 or GLM 5.2 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both GLM 5.1 and GLM 5.2, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, GLM 5.1 or GLM 5.2?+

GLM 5.1 is $1.05 and GLM 5.2 is $0.75 per million tokens, so GLM 5.2 is cheaper on this metric. GLM 5.1 is $3.50 and GLM 5.2 is $2.40 per million tokens, so GLM 5.2 is cheaper on this metric.

Which has a larger context window, GLM 5.1 or GLM 5.2?+

GLM 5.2 has the larger sourced context window. GLM 5.1 supports 202,752 tokens and GLM 5.2 supports 1,048,576 tokens.

Which performs better in benchmarks, GLM 5.1 or GLM 5.2?+

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Can GLM 5.1 or GLM 5.2 be self-hosted?+

Both models have the same sourced self-hosting status: supported. GLM 5.1 is open weight; GLM 5.2 is open weight.

Can GLM 5.1 and GLM 5.2 understand images?+

GLM 5.1 is not documented with image input; GLM 5.2 is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, GLM 5.1 or GLM 5.2?+

Neither model has a sourced maximum output in this comparison, so a larger limit cannot be determined.

Do GLM 5.1 and GLM 5.2 support reasoning and tool use?+

GLM 5.1: reasoning and tool calling. GLM 5.2: reasoning and tool calling. Feature support does not establish relative quality.

Which is available from more inference providers, GLM 5.1 or GLM 5.2?+

GLM 5.1 has 4 sourced provider routes; GLM 5.2 has 5, so GLM 5.2 has broader tracked availability.

Which offers better value, GLM 5.1 or GLM 5.2?+

There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.

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