Mistral Medium 3.5 128B vs Bonsai 1.7B
At a Glance
| Compare | Mistral Medium 3.5 128BMistral AI | Bonsai 1.7BPrismML |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | 262K | 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 | Mistral-Medium-3.5-128B | Bonsai 1.7B |
|---|---|---|
| Developer | Mistral AI | PrismML |
| Family | Mistral Medium 3 5 128b | Bonsai 1 7b |
| Model | Mistral-Medium-3.5-128B | Bonsai 1.7B |
| Version | Mistral-Medium-3.5-128B | Bonsai 1.7B |
| Lifecycle | active | active |
| Released | Unknown | 2026-03-29 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 262K | 33K |
| Total parameters | 127.7B | 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 | chat, generation, reasoning, tools | chat, generation |
| Effective bit width | Unknown | 1 bit per weight |
| Weight size | Unknown | 0.25 GB |
| Weight format | Unknown | Binary Q1_0 |
Mistral Medium 3.5 128B Capabilities
Bonsai 1.7B Capabilities
Primary Evidence
Sources and Freshness
Questions
Mistral Medium 3.5 128B vs Bonsai 1.7B FAQs
Is Mistral Medium 3.5 128B or Bonsai 1.7B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Mistral Medium 3.5 128B and Bonsai 1.7B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Mistral Medium 3.5 128B or 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, Mistral Medium 3.5 128B or Bonsai 1.7B?+
Mistral Medium 3.5 128B has the larger sourced context window. Mistral Medium 3.5 128B supports 262K and Bonsai 1.7B supports 33K.
Which performs better in benchmarks, Mistral Medium 3.5 128B or Bonsai 1.7B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Mistral Medium 3.5 128B or Bonsai 1.7B be self-hosted?+
Both models have the same recorded self-hosting status: supported. Mistral Medium 3.5 128B is open weight; Bonsai 1.7B is open weight.
Can Mistral Medium 3.5 128B and Bonsai 1.7B understand images?+
Mistral Medium 3.5 128B is documented with image input; Bonsai 1.7B is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Mistral Medium 3.5 128B or Bonsai 1.7B?+
Neither has a larger sourced maximum output. Mistral Medium 3.5 128B is — and Bonsai 1.7B is —.
Do Mistral Medium 3.5 128B and Bonsai 1.7B support reasoning and tool use?+
Mistral Medium 3.5 128B: reasoning, tool calling, and image input. Bonsai 1.7B: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, Mistral Medium 3.5 128B or Bonsai 1.7B?+
Mistral Medium 3.5 128B has 0 sourced provider routes; Bonsai 1.7B has 0, a tie.
Which offers better value, Mistral Medium 3.5 128B or 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.