Qwen3.8 27B vs GLM OCR

At a Glance

Compare
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5#30 of 4654.0 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 36.0–69.4UnrankedNot in the 46-model eligible cohort
CostLower is better · Published-token output estimate#16 of 44$0.072 per LiveBench caseUnrankedNot in the 44-model eligible cohort
EfficiencyHigher is better · MM Efficiency v1.5#17 of 3854.8 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 45.8–62.5UnrankedNot in the 38-model eligible cohort
Pricing and Limits
Input priceFrom · USD / 1M tokens$0.20Deepinfra · Sep 22, 2026Not reported
Output priceFrom · USD / 1M tokens$2.50Deepinfra · Sep 22, 2026Not reported
Context windowMaximum documented tokens262K131K
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 →
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

FieldQwen3.8-27BGLM-OCR
DeveloperQwenZ.ai
FamilyQwen3 8 27bGlm OCR
ModelQwen3.8-27BGLM-OCR
VersionQwen3.8-27BGLM-OCR
Lifecycleactiveactive
ReleasedUnknownUnknown
Knowledge cutoffUnknownUnknown
Input modalitiesText, ImageText, Image
Output modalitiesTextText
Context window262K131K
Total parameters27.8B1.3B
Active parametersUnknownUnknown
Licenseapache-2.0mit
Open weightsYesYes
API availableYesYes
Self-hostableYesYes
Provider accessDeepinfra (Standard), Hugging Face (Standard), Openrouter (Standard)Together Ai (Standard)
Capabilitieschat, generation, reasoning, toolschat, generation, tools

Qwen3.8 27B Capabilities

chatgenerationreasoningtools
Serving providers3
Canonical IDQwen/Qwen3.8-27B

GLM OCR Capabilities

chatgenerationtools
Serving providers1
Canonical IDzai-org/GLM-OCR

Primary Evidence

Sources and Freshness

Questions

Qwen3.8 27B vs GLM OCR FAQs

Is Qwen3.8 27B or GLM OCR better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.8 27B and GLM OCR, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Qwen3.8 27B or GLM OCR?+

Only Qwen3.8 27B has a directly sourced input price: $0.20 per million tokens. Only Qwen3.8 27B has a directly sourced output price: $2.50 per million tokens.

Which has a larger context window, Qwen3.8 27B or GLM OCR?+

Qwen3.8 27B has the larger sourced context window. Qwen3.8 27B supports 262K and GLM OCR supports 131K.

Which performs better in benchmarks, Qwen3.8 27B or GLM OCR?+

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

Can Qwen3.8 27B or GLM OCR be self-hosted?+

Both models have the same recorded self-hosting status: supported. Qwen3.8 27B is open weight; GLM OCR is open weight.

Can Qwen3.8 27B and GLM OCR understand images?+

Qwen3.8 27B is documented with image input; GLM OCR is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Qwen3.8 27B or GLM OCR?+

Neither has a larger sourced maximum output. Qwen3.8 27B is 131K and GLM OCR is —.

Do Qwen3.8 27B and GLM OCR support reasoning and tool use?+

Qwen3.8 27B: reasoning, tool calling, and image input. GLM OCR: tool calling and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Qwen3.8 27B or GLM OCR?+

Qwen3.8 27B has 3 sourced provider routes; GLM OCR has 1, so Qwen3.8 27B has broader tracked availability.

Which offers better value, Qwen3.8 27B or GLM OCR?+

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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