Granite Embedding English r2 vs GLM 5.3 Flash

At a Glance

Compare
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5UnrankedNot in the 46-model eligible cohort#29 of 4654.6 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 36.4–69.8
CostLower is better · Published-token output estimateUnrankedNot in the 44-model eligible cohort#2 of 44$0.0087 per LiveBench case
EfficiencyHigher is better · MM Efficiency v1.5UnrankedNot in the 38-model eligible cohort#1 of 3877.3 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 68.2–84.9
Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$0.075Z.ai · Aug 29, 2026
Output priceFrom · USD / 1M tokensNot reported$0.25Z.ai · Aug 29, 2026
Context windowMaximum documented tokens8K1,000K
Model facts checkedAug 28, 2026View model evidence →Sep 2, 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-5.3-Flash
DeveloperIBMZ.ai
FamilyGranite Embedding English R2Glm 5 3 Flash
Modelgranite-embedding-english-r2GLM-5.3-Flash
Versiongranite-embedding-english-r2GLM-5.3-Flash
Lifecycleactiveactive
Released2025-08-152026-09-02
Knowledge cutoffUnknownUnknown
Input modalitiesTextText, Image, Video, Document
Output modalitiesEmbeddingText
Context window8K1,000K
Total parameters149M320B
Active parametersUnknown18B
Licenseapache-2.0MIT
Open weightsYesYes
API availableUnknownYes
Self-hostableYesYes
Provider accessUnknownDeepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard), Z.ai (Standard)
Capabilitiesembeddingsagents, chat, computer-use, reasoning, structured_outputs, tools, vision

Granite Embedding English r2 Capabilities

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

GLM 5.3 Flash Capabilities

agentschatcomputer-usereasoningstructured outputstoolsvision
Serving providers4
Canonical IDzai-org/glm-5.3-flash

Primary Evidence

Sources and Freshness

Questions

Granite Embedding English r2 vs GLM 5.3 Flash FAQs

Is Granite Embedding English r2 or GLM 5.3 Flash better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Granite Embedding English r2 and GLM 5.3 Flash, 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 5.3 Flash?+

Only GLM 5.3 Flash has a directly sourced input price: $0.075 per million tokens. Only GLM 5.3 Flash has a directly sourced output price: $0.25 per million tokens.

Which has a larger context window, Granite Embedding English r2 or GLM 5.3 Flash?+

GLM 5.3 Flash has the larger sourced context window. Granite Embedding English r2 supports 8K and GLM 5.3 Flash supports 1,000K.

Which performs better in benchmarks, Granite Embedding English r2 or GLM 5.3 Flash?+

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 5.3 Flash be self-hosted?+

Both models have the same recorded self-hosting status: supported. Granite Embedding English r2 is open weight; GLM 5.3 Flash is open weight.

Can Granite Embedding English r2 and GLM 5.3 Flash understand images?+

Granite Embedding English r2 is not documented with image input; GLM 5.3 Flash is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Granite Embedding English r2 or GLM 5.3 Flash?+

Neither has a larger sourced maximum output. Granite Embedding English r2 is — and GLM 5.3 Flash is 131K.

Do Granite Embedding English r2 and GLM 5.3 Flash support reasoning and tool use?+

Granite Embedding English r2: none of these features are definitively sourced. GLM 5.3 Flash: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Granite Embedding English r2 or GLM 5.3 Flash?+

Granite Embedding English r2 has 0 sourced provider routes; GLM 5.3 Flash has 4, so GLM 5.3 Flash has broader tracked availability.

Which offers better value, Granite Embedding English r2 or GLM 5.3 Flash?+

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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