Mistral Medium 3.5 128B vs Ternary Bonsai 27B
At a Glance
| Compare |  Mistral Medium 3.5 128BMistral AI |  Ternary Bonsai 27BPrismML |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | 262K | 262K |
| 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 | Ternary Bonsai 27B |
|---|---|---|
| Developer | Mistral AI | PrismML |
| Family | Mistral Medium 3 5 128b | Bonsai 27b |
| Model | Mistral-Medium-3.5-128B | Ternary Bonsai 27B |
| Version | Mistral-Medium-3.5-128B | Ternary Bonsai 27B |
| Lifecycle | active | active |
| Released | Unknown | 2026-07-04 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 262K | 262K |
| Total parameters | 127.7B | 27B |
| Active parameters | Unknown | Unknown |
| License | other | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Unknown | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Together Ai (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning, tools, vision |
| Base model | Unknown | Qwen3.6 27B |
| Effective bit width | Unknown | 1.58 bits per weight |
| Language model size | Unknown | 6.66 GiB |
| Weight format | Unknown | Ternary Q2_0 |
Mistral Medium 3.5 128B Capabilities
Ternary Bonsai 27B Capabilities
Related Comparisons
All image comparisons →| A | Pair | B | Context |
|---|---|---|---|
Mistral-Medium-3.5-128BMistral AI | vs |  GPT-6 AstraOpenAI | cross-developer peersimage, text |
Claude Fable 5.1Anthropic | vs | Mistral-Medium-3.5-128BMistral AI | cross-developer peersimage, text |
 Gemini 3.1 ProGoogle DeepMind | vs | Mistral-Medium-3.5-128BMistral AI | cross-developer peerstext |
 DeepSeek-V4.1-FlashDeepSeek | vs | Mistral-Medium-3.5-128BMistral AI | cross-developer peersimage, text |
Mistral-Medium-3.5-128BMistral AI | vs | Grok 4.6xAI | cross-developer peersimage, text |
Mistral-Medium-3.5-128BMistral AI | vs |  Qwen3.8-MaxQwen | cross-developer peersimage, text |
Mistral-Medium-3.5-128BMistral AI | vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text |
 MiniMax-M3MiniMax | vs | Mistral-Medium-3.5-128BMistral AI | cross-developer peersimage, text |
Mistral-Medium-3.5-128BMistral AI | vs |  GLM-5.3Z.ai | cross-developer peerstext |
Mistral-Medium-3.5-128BMistral AI | vs |  Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | Mistral-Medium-3.5-128BMistral AI | cross-developer peersimage, text |
Mistral-Medium-3.5-128BMistral AI | vs | Mistral Large 3Mistral AI | family variantsimage, text |
Primary Evidence
Sources and Freshness
Questions
Mistral Medium 3.5 128B vs Ternary Bonsai 27B FAQs
Is Mistral Medium 3.5 128B or Ternary Bonsai 27B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Mistral Medium 3.5 128B and Ternary Bonsai 27B, 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 Ternary Bonsai 27B?+
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 Ternary Bonsai 27B?+
Neither model has a larger sourced context window in this comparison. Mistral Medium 3.5 128B is 262K and Ternary Bonsai 27B is 262K.
Which performs better in benchmarks, Mistral Medium 3.5 128B or Ternary Bonsai 27B?+
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 Ternary Bonsai 27B be self-hosted?+
Both models have the same recorded self-hosting status: supported. Mistral Medium 3.5 128B is open weight; Ternary Bonsai 27B is open weight.
Can Mistral Medium 3.5 128B and Ternary Bonsai 27B understand images?+
Mistral Medium 3.5 128B is documented with image input; Ternary Bonsai 27B is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Mistral Medium 3.5 128B or Ternary Bonsai 27B?+
Neither has a larger sourced maximum output. Mistral Medium 3.5 128B is — and Ternary Bonsai 27B is —.
Do Mistral Medium 3.5 128B and Ternary Bonsai 27B support reasoning and tool use?+
Mistral Medium 3.5 128B: reasoning, tool calling, and image input. Ternary Bonsai 27B: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Mistral Medium 3.5 128B or Ternary Bonsai 27B?+
Mistral Medium 3.5 128B has 0 sourced provider routes; Ternary Bonsai 27B has 1, so Ternary Bonsai 27B has broader tracked availability.
Which offers better value, Mistral Medium 3.5 128B or Ternary Bonsai 27B?+
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.