Granite Embedding English r2 vs GLM 5.1

At a Glance

Compare
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 tokens8K203K
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-english-r2GLM-5.1
DeveloperIBMZ.ai
FamilyGranite Embedding English R2Glm 5 1
Modelgranite-embedding-english-r2GLM-5.1
Versiongranite-embedding-english-r2GLM-5.1
Lifecycleactiveactive
Released2025-08-15Unknown
Knowledge cutoffUnknownUnknown
Input modalitiesTextText
Output modalitiesEmbeddingText
Context window8K203K
Total parameters149M753.9B
Active parametersUnknownUnknown
Licenseapache-2.0mit
Open weightsYesYes
API availableUnknownYes
Self-hostableYesYes
Provider accessUnknownDeepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard)
Capabilitiesembeddingschat, generation, reasoning, tools

Granite Embedding English r2 Capabilities

embeddings
Serving providers0
Canonical IDibm-granite/granite-embedding-english-r2

GLM 5.1 Capabilities

chatgenerationreasoningtools
Serving providers4
Canonical IDzai-org/GLM-5.1

Primary Evidence

Sources and Freshness

Questions

Granite Embedding English r2 vs GLM 5.1 FAQs

Is Granite Embedding English r2 or GLM 5.1 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Granite Embedding English r2 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 English r2 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 English r2 or GLM 5.1?+

GLM 5.1 has the larger sourced context window. Granite Embedding English r2 supports 8K and GLM 5.1 supports 203K.

Which performs better in benchmarks, Granite Embedding English r2 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 English r2 or GLM 5.1 be self-hosted?+

Both models have the same recorded self-hosting status: supported. Granite Embedding English r2 is open weight; GLM 5.1 is open weight.

Can Granite Embedding English r2 and GLM 5.1 understand images?+

Granite Embedding English r2 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 English r2 or GLM 5.1?+

Neither has a larger sourced maximum output. Granite Embedding English r2 is — and GLM 5.1 is —.

Do Granite Embedding English r2 and GLM 5.1 support reasoning and tool use?+

Granite Embedding English r2: 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 English r2 or GLM 5.1?+

Granite Embedding English r2 has 0 sourced provider routes; GLM 5.1 has 4, so GLM 5.1 has broader tracked availability.

Which offers better value, Granite Embedding English r2 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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