Claude Mythos 5.1 vs Ternary Bonsai 4B

At a Glance

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Pricing and Limits
Input priceFrom · USD / 1M tokens$10.00Anthropic · Sep 2, 2026Not reported
Output priceFrom · USD / 1M tokens$50.00Anthropic · Sep 2, 2026Not reported
Context windowMaximum documented tokens1,000K33K
Model facts checkedSep 2, 2026View model evidence →Sep 18, 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

FieldClaude Mythos 5.1Ternary Bonsai 4B
DeveloperAnthropicPrismML
FamilyClaude 5 1Bonsai 4b
ModelClaude Mythos 5.1Ternary Bonsai 4B
VersionClaude Mythos 5.1Ternary Bonsai 4B
Lifecycleactiveactive
Released2026-09-012026-04-18
Knowledge cutoff2026-06-01Unknown
Input modalitiesText, ImageText
Output modalitiesTextText
Context window1,000K33K
Total parametersUnknown4B
Active parametersUnknownUnknown
LicenseUnknownapache-2.0
Open weightsNoYes
API availableYesNo
Self-hostableNoYes
Provider accessAnthropic (Project Glasswing)Unknown
Capabilitieschat, generation, reasoning, toolschat, generation
Effective bit widthUnknown1.58 bits per weight
Weight sizeUnknown1.07 GB
Weight formatUnknownTernary Q2_0

Claude Mythos 5.1 Capabilities

chatgenerationreasoningtools
Serving providers1
Canonical IDanthropic/claude-mythos-5-1

Ternary Bonsai 4B Capabilities

chatgeneration
Serving providers0
Canonical IDprism-ml/Ternary-Bonsai-4B

Primary Evidence

Sources and Freshness

Questions

Claude Mythos 5.1 vs Ternary Bonsai 4B FAQs

Is Claude Mythos 5.1 or Ternary Bonsai 4B better for coding?+

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

Which is cheaper, Claude Mythos 5.1 or Ternary Bonsai 4B?+

Only Claude Mythos 5.1 has a directly sourced input price: $10.00 per million tokens. Only Claude Mythos 5.1 has a directly sourced output price: $50.00 per million tokens.

Which has a larger context window, Claude Mythos 5.1 or Ternary Bonsai 4B?+

Claude Mythos 5.1 has the larger sourced context window. Claude Mythos 5.1 supports 1,000K and Ternary Bonsai 4B supports 33K.

Which performs better in benchmarks, Claude Mythos 5.1 or Ternary Bonsai 4B?+

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

Can Claude Mythos 5.1 or Ternary Bonsai 4B be self-hosted?+

Ternary Bonsai 4B is the only model in this pair currently marked as self-hostable. Claude Mythos 5.1 is not marked open weight; Ternary Bonsai 4B is open weight.

Can Claude Mythos 5.1 and Ternary Bonsai 4B understand images?+

Claude Mythos 5.1 is documented with image input; Ternary Bonsai 4B is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Claude Mythos 5.1 or Ternary Bonsai 4B?+

Neither has a larger sourced maximum output. Claude Mythos 5.1 is 128K and Ternary Bonsai 4B is —.

Do Claude Mythos 5.1 and Ternary Bonsai 4B support reasoning and tool use?+

Claude Mythos 5.1: reasoning, tool calling, and image input. Ternary Bonsai 4B: none of these features are definitively sourced. Feature support does not establish relative quality.

Which is available from more inference providers, Claude Mythos 5.1 or Ternary Bonsai 4B?+

Claude Mythos 5.1 has 1 sourced provider route; Ternary Bonsai 4B has 0, so Claude Mythos 5.1 has broader tracked availability.

Which offers better value, Claude Mythos 5.1 or Ternary Bonsai 4B?+

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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