Codestral 22B v0.1 vs Ternary Bonsai 1.7B
At a Glance
| Compare | Codestral 22B v0.1Mistral AI | Ternary Bonsai 1.7BPrismML |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | 33K | 33K |
| Model facts checked | Aug 28, 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
Side-by-Side Facts
| Field | Codestral-22B-v0.1 | Ternary Bonsai 1.7B |
|---|---|---|
| Developer | Mistral AI | PrismML |
| Family | Codestral 22b V0 1 | Bonsai 1 7b |
| Model | Codestral-22B-v0.1 | Ternary Bonsai 1.7B |
| Version | Codestral-22B-v0.1 | Ternary Bonsai 1.7B |
| Lifecycle | active | active |
| Released | Unknown | 2026-04-18 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 33K | 33K |
| Total parameters | 22.2B | 1.7B |
| Active parameters | Unknown | Unknown |
| License | other | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Unknown | No |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Unknown |
| Capabilities | generation | chat, generation |
| Effective bit width | Unknown | 1.58 bits per weight |
| Weight size | Unknown | 0.46 GB |
| Weight format | Unknown | Ternary Q2_0 |
Codestral 22B v0.1 Capabilities
Ternary Bonsai 1.7B Capabilities
Primary Evidence
Sources and Freshness
Questions
Codestral 22B v0.1 vs Ternary Bonsai 1.7B FAQs
Is Codestral 22B v0.1 or Ternary Bonsai 1.7B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Codestral 22B v0.1 and Ternary Bonsai 1.7B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Codestral 22B v0.1 or Ternary Bonsai 1.7B?+
Neither model has a directly sourced input price in this comparison. Neither model has a directly sourced output price in this comparison.
Which has a larger context window, Codestral 22B v0.1 or Ternary Bonsai 1.7B?+
Neither model has a larger sourced context window in this comparison. Codestral 22B v0.1 is 33K and Ternary Bonsai 1.7B is 33K.
Which performs better in benchmarks, Codestral 22B v0.1 or Ternary Bonsai 1.7B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Codestral 22B v0.1 or Ternary Bonsai 1.7B be self-hosted?+
Both models have the same recorded self-hosting status: supported. Codestral 22B v0.1 is open weight; Ternary Bonsai 1.7B is open weight.
Can Codestral 22B v0.1 and Ternary Bonsai 1.7B understand images?+
Codestral 22B v0.1 is not documented with image input; Ternary Bonsai 1.7B is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Codestral 22B v0.1 or Ternary Bonsai 1.7B?+
Neither has a larger sourced maximum output. Codestral 22B v0.1 is — and Ternary Bonsai 1.7B is —.
Do Codestral 22B v0.1 and Ternary Bonsai 1.7B support reasoning and tool use?+
Codestral 22B v0.1: none of these features are definitively sourced. Ternary Bonsai 1.7B: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, Codestral 22B v0.1 or Ternary Bonsai 1.7B?+
Codestral 22B v0.1 has 0 sourced provider routes; Ternary Bonsai 1.7B has 0, a tie.
Which offers better value, Codestral 22B v0.1 or Ternary Bonsai 1.7B?+
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.