Sonar vs GLM 5.2

At a Glance

Compare
SonarPerplexity
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5UnrankedNot in the 46-model eligible cohort#23 of 4661.9 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 41.3–74.6
CostLower is better · Published-token output estimateUnrankedNot in the 44-model eligible cohort#12 of 44$0.056 per LiveBench case
EfficiencyHigher is better · MM Efficiency v1.5UnrankedNot in the 38-model eligible cohort#7 of 3861.3 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 51.0–67.6
Pricing and Limits
Input priceFrom · USD / 1M tokens$1.00Openrouter · Sep 22, 2026$0.75Deepinfra · Sep 22, 2026
Output priceFrom · USD / 1M tokens$1.00Openrouter · Sep 22, 2026$2.40Deepinfra · Sep 22, 2026
Context windowMaximum documented tokens128K1,049K
Model facts checkedAug 29, 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 →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

FieldSonarGLM-5.2
DeveloperPerplexityZ.ai
FamilySonarGlm 5 2
ModelSonarGLM-5.2
VersionSonarGLM-5.2
Lifecycleactiveactive
ReleasedUnknownUnknown
Knowledge cutoffUnknownUnknown
Input modalitiesTextText
Output modalitiesTextText
Context window128K1,049K
Total parametersUnknown753.3B
Active parametersUnknownUnknown
LicenseUnknownmit
Open weightsNoYes
API availableYesYes
Self-hostableNoYes
Provider accessOpenrouter (Standard), Perplexity (Standard)Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard)
Capabilitieschat, citations, searchchat, generation, reasoning, tools

Sonar Capabilities

chatcitationssearch
Serving providers2
Canonical IDperplexity/sonar

GLM 5.2 Capabilities

chatgenerationreasoningtools
Serving providers5
Canonical IDzai-org/GLM-5.2

Primary Evidence

Sources and Freshness

Questions

Sonar vs GLM 5.2 FAQs

Is Sonar or GLM 5.2 better for coding?+

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

Which is cheaper, Sonar or GLM 5.2?+

Sonar is $1.00 and GLM 5.2 is $0.75 per million tokens, so GLM 5.2 is cheaper on this metric. Sonar is $1.00 and GLM 5.2 is $2.40 per million tokens, so Sonar is cheaper on this metric.

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

GLM 5.2 has the larger sourced context window. Sonar supports 128K and GLM 5.2 supports 1,049K.

Which performs better in benchmarks, Sonar 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 Sonar or GLM 5.2 be self-hosted?+

GLM 5.2 is the only model in this pair currently marked as self-hostable. Sonar is not marked open weight; GLM 5.2 is open weight.

Can Sonar and GLM 5.2 understand images?+

Sonar 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, Sonar or GLM 5.2?+

Neither has a larger sourced maximum output. Sonar is — and GLM 5.2 is —.

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

Sonar: none of these features are definitively sourced. GLM 5.2: reasoning and tool calling. Feature support does not establish relative quality.

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

Sonar has 2 sourced provider routes; GLM 5.2 has 5, so GLM 5.2 has broader tracked availability.

Which offers better value, Sonar 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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