Llama 4 Scout 17B 16E Instruct vs GLM OCR

At a Glance

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Pricing and Limits
Input priceFrom · USD / 1M tokens$0.10Deepinfra · Sep 22, 2026Not reported
Output priceFrom · USD / 1M tokens$0.30Deepinfra · Sep 22, 2026Not reported
Context windowMaximum documented tokens10,000K131K
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

FieldLlama-4-Scout-17B-16E-InstructGLM-OCR
DeveloperMetaZ.ai
FamilyLlama 4 Scout 17b 16e InstructGlm OCR
ModelLlama-4-Scout-17B-16E-InstructGLM-OCR
VersionLlama-4-Scout-17B-16E-InstructGLM-OCR
Lifecycleactiveactive
Released2025-04-05Unknown
Knowledge cutoffUnknownUnknown
Input modalitiesText, ImageText, Image
Output modalitiesTextText
Context window10,000K131K
Total parameters108.6B1.3B
Active parameters17BUnknown
Licenseothermit
Open weightsYesYes
API availableYesYes
Self-hostableYesYes
Provider accessDeepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard)Together Ai (Standard)
Capabilitieschat, generation, toolschat, generation, tools

Llama 4 Scout 17B 16E Instruct Capabilities

chatgenerationtools
Serving providers4
Canonical IDmeta-llama/Llama-4-Scout-17B-16E-Instruct

GLM OCR Capabilities

chatgenerationtools
Serving providers1
Canonical IDzai-org/GLM-OCR

Primary Evidence

Sources and Freshness

Questions

Llama 4 Scout 17B 16E Instruct vs GLM OCR FAQs

Is Llama 4 Scout 17B 16E Instruct or GLM OCR better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Llama 4 Scout 17B 16E Instruct and GLM OCR, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Llama 4 Scout 17B 16E Instruct or GLM OCR?+

Only Llama 4 Scout 17B 16E Instruct has a directly sourced input price: $0.10 per million tokens. Only Llama 4 Scout 17B 16E Instruct has a directly sourced output price: $0.30 per million tokens.

Which has a larger context window, Llama 4 Scout 17B 16E Instruct or GLM OCR?+

Llama 4 Scout 17B 16E Instruct has the larger sourced context window. Llama 4 Scout 17B 16E Instruct supports 10,000K and GLM OCR supports 131K.

Which performs better in benchmarks, Llama 4 Scout 17B 16E Instruct or GLM OCR?+

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

Can Llama 4 Scout 17B 16E Instruct or GLM OCR be self-hosted?+

Both models have the same recorded self-hosting status: supported. Llama 4 Scout 17B 16E Instruct is open weight; GLM OCR is open weight.

Can Llama 4 Scout 17B 16E Instruct and GLM OCR understand images?+

Llama 4 Scout 17B 16E Instruct 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, Llama 4 Scout 17B 16E Instruct or GLM OCR?+

Neither has a larger sourced maximum output. Llama 4 Scout 17B 16E Instruct is — and GLM OCR is —.

Do Llama 4 Scout 17B 16E Instruct and GLM OCR support reasoning and tool use?+

Llama 4 Scout 17B 16E Instruct: 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, Llama 4 Scout 17B 16E Instruct or GLM OCR?+

Llama 4 Scout 17B 16E Instruct has 4 sourced provider routes; GLM OCR has 1, so Llama 4 Scout 17B 16E Instruct has broader tracked availability.

Which offers better value, Llama 4 Scout 17B 16E Instruct 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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