Granite Embedding 30m English vs GLM 5.1

At a Glance

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Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$1.05Deepinfra · Sep 23, 2026
Output priceFrom · USD / 1M tokensNot reported$3.50Deepinfra · Sep 23, 2026
Context windowMaximum documented tokens1K203K
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 →

Different Model RolesThese models do not share a sourced market category. Their primary-source facts remain comparable below, while performance claims require matched evidence.

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

Fieldgranite-embedding-30m-englishGLM-5.1
DeveloperIBMZ.ai
FamilyGranite Embedding 30m EnglishGlm 5 1
Modelgranite-embedding-30m-englishGLM-5.1
Versiongranite-embedding-30m-englishGLM-5.1
Lifecycleactiveactive
Released2025-08-29Unknown
Knowledge cutoffUnknownUnknown
Input modalitiesTextText
Output modalitiesEmbeddingText
Context window1K203K
Total parameters30.3M753.9B
Active parametersUnknownUnknown
Licenseapache-2.0mit
Open weightsYesYes
API availableYesYes
Self-hostableYesYes
Provider accessHugging Face (Standard)Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard)
Capabilitiesembeddingschat, generation, reasoning, tools

Granite Embedding 30m English Capabilities

embeddings
Serving providers1
Canonical IDibm-granite/granite-embedding-30m-english

GLM 5.1 Capabilities

chatgenerationreasoningtools
Serving providers4
Canonical IDzai-org/GLM-5.1

Primary Evidence

Sources and Freshness

Questions

Granite Embedding 30m English vs GLM 5.1 FAQs

Is Granite Embedding 30m English or GLM 5.1 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Granite Embedding 30m English and GLM 5.1, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Granite Embedding 30m English or GLM 5.1?+

Only GLM 5.1 has a directly sourced input price: $1.05 per million tokens. Only GLM 5.1 has a directly sourced output price: $3.50 per million tokens.

Which has a larger context window, Granite Embedding 30m English or GLM 5.1?+

GLM 5.1 has the larger sourced context window. Granite Embedding 30m English supports 1K and GLM 5.1 supports 203K.

Which performs better in benchmarks, Granite Embedding 30m English or GLM 5.1?+

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

Can Granite Embedding 30m English or GLM 5.1 be self-hosted?+

Both models have the same recorded self-hosting status: supported. Granite Embedding 30m English is open weight; GLM 5.1 is open weight.

Can Granite Embedding 30m English and GLM 5.1 understand images?+

Granite Embedding 30m English is not documented with image input; GLM 5.1 is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Granite Embedding 30m English or GLM 5.1?+

Neither has a larger sourced maximum output. Granite Embedding 30m English is — and GLM 5.1 is —.

Do Granite Embedding 30m English and GLM 5.1 support reasoning and tool use?+

Granite Embedding 30m English: none of these features are definitively sourced. GLM 5.1: reasoning and tool calling. Feature support does not establish relative quality.

Which is available from more inference providers, Granite Embedding 30m English or GLM 5.1?+

Granite Embedding 30m English has 1 sourced provider route; GLM 5.1 has 4, so GLM 5.1 has broader tracked availability.

Which offers better value, Granite Embedding 30m English or GLM 5.1?+

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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