Bonsai Image Binary 4B 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 tokensNot reported203K
Model facts checkedSep 18, 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

FieldBonsai Image Binary 4BGLM-5.1
DeveloperPrismMLZ.ai
FamilyBonsai Image 4bGlm 5 1
ModelBonsai Image Binary 4BGLM-5.1
VersionBonsai Image Binary 4BGLM-5.1
Lifecycleactiveactive
Released2026-05-18Unknown
Knowledge cutoffUnknownUnknown
Input modalitiesTextText
Output modalitiesImageText
Context windowUnknown203K
Total parameters4B753.9B
Active parametersUnknownUnknown
Licenseapache-2.0mit
Open weightsYesYes
API availableNoYes
Self-hostableYesYes
Provider accessUnknownDeepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard)
Capabilitiesgenerationchat, generation, reasoning, tools
Base modelFLUX.2 Klein 4BUnknown
Default resolution512 × 512Unknown
Transformer size0.93 GBUnknown
Weight formatBinary weights with FP16 group scalesUnknown

Bonsai Image Binary 4B Capabilities

generation
Serving providers0
Canonical IDprism-ml/Bonsai-Image-Binary-4B

GLM 5.1 Capabilities

chatgenerationreasoningtools
Serving providers4
Canonical IDzai-org/GLM-5.1

Primary Evidence

Sources and Freshness

Questions

Bonsai Image Binary 4B vs GLM 5.1 FAQs

Is Bonsai Image Binary 4B or GLM 5.1 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Bonsai Image Binary 4B and GLM 5.1, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Bonsai Image Binary 4B 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, Bonsai Image Binary 4B or GLM 5.1?+

Neither model has a larger sourced context window in this comparison. Bonsai Image Binary 4B is — and GLM 5.1 is 203K.

Which performs better in benchmarks, Bonsai Image Binary 4B 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 Bonsai Image Binary 4B or GLM 5.1 be self-hosted?+

Both models have the same recorded self-hosting status: supported. Bonsai Image Binary 4B is open weight; GLM 5.1 is open weight.

Can Bonsai Image Binary 4B and GLM 5.1 understand images?+

Bonsai Image Binary 4B 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, Bonsai Image Binary 4B or GLM 5.1?+

Neither has a larger sourced maximum output. Bonsai Image Binary 4B is — and GLM 5.1 is —.

Do Bonsai Image Binary 4B and GLM 5.1 support reasoning and tool use?+

Bonsai Image Binary 4B: 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, Bonsai Image Binary 4B or GLM 5.1?+

Bonsai Image Binary 4B has 0 sourced provider routes; GLM 5.1 has 4, so GLM 5.1 has broader tracked availability.

Which offers better value, Bonsai Image Binary 4B 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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