Model comparison
Mistral Medium 3.5 128B vs Ternary Bonsai 1.7B
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Evidence parity. Mistral Medium 3.5 128B and Ternary Bonsai 1.7B share 0 comparable benchmark results. 0 of 8 categories are comparable. 25 results are unique to Mistral Medium 3.5 128B; 0 to Ternary Bonsai 1.7B.
Updated July 27, 2026- Shared results
- 0
- Mistral Medium 3.5 128B only
- 25
- Ternary Bonsai 1.7B only
- 0
- Comparable categories
- 0 / 8
Benchmark data for Mistral Medium 3.5 128B and Ternary Bonsai 1.7B is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM does not have sourced benchmark coverage for Ternary Bonsai 1.7B yet. This comparison is currently limited to metadata such as context window, reasoning mode, and pricing where available.
Mistral Medium 3.5 128B is priced at $1.50 input / $7.50 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Ternary Bonsai 1.7B. Mistral Medium 3.5 128B has the larger context window at 256K, compared with 32K for Ternary Bonsai 1.7B.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Mistral Medium 3.5 128B | Ternary Bonsai 1.7B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Mistral Medium 3.5 128B$1.5 input / $7.5 output | Ternary Bonsai 1.7B$0 input / $0 output | Ternary Bonsai 1.7B has the lower combined listed price. |
| Generation speedtokens per second | Mistral Medium 3.5 128BNot available | Ternary Bonsai 1.7BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Mistral Medium 3.5 128BNot available | Ternary Bonsai 1.7BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Mistral Medium 3.5 128B256K | Ternary Bonsai 1.7B32K | Mistral Medium 3.5 128B lists the larger context window. |
Benchmark Deep Dive
Agentic11 benchmarks
| Benchmark | Mistral Medium 3.5 128B | Ternary Bonsai 1.7B | Result |
|---|---|---|---|
| τ³-bench resultsSource | 91.4% | — | Not comparable |
| AA Agentic IndexSource | 19.0% | — | Not comparable |
| τ²-bench resultsSource | 94.2% | — | Not comparable |
| GDPval-AASource | 21.6% | — | Not comparable |
| GDPval-AASource | 933 | — | Not comparable |
| Gert LabsSource | 39.10% | — | Not comparable |
| AA EnterpriseOps-GymSource | 33.7% | — | Not comparable |
| AA Harvey LABSource | 69.1% | — | Not comparable |
| terminalBenchHardSource | 33.3% | — | Not comparable |
| AA BriefcaseSource | 516 | — | Not comparable |
| AA Tau3 BankingSource | 14.4% | — | Not comparable |
Coding3 benchmarks
Reasoning2 benchmarks
Knowledge7 benchmarks
| Benchmark | Mistral Medium 3.5 128B | Ternary Bonsai 1.7B | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 29.9% | — | Not comparable |
| AA-GPQA DiamondSource | 74.8% | — | Not comparable |
| AA-HLESource | 12.8% | — | Not comparable |
| AA-Omniscience IndexSource | -36.3% | — | Not comparable |
| AA-Omniscience AccuracySource | 25.1% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 82.0% | — | Not comparable |
| AA Openness IndexSource | 33.3% | — | Not comparable |
Multimodal1 benchmarks
| Benchmark | Mistral Medium 3.5 128B | Ternary Bonsai 1.7B | Result |
|---|---|---|---|
| AA-MMMU-ProSource | 64.9% | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Mistral Medium 3.5 128B | Ternary Bonsai 1.7B | Result |
|---|---|---|---|
| AA-IFBenchSource | 68.8% | — | Not comparable |
Frequently Asked Questions (3)
Can I compare Mistral Medium 3.5 128B and Ternary Bonsai 1.7B on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for Mistral Medium 3.5 128B and Ternary Bonsai 1.7B today?
Mistral Medium 3.5 128B: $1.50 input / $7.50 output per 1M tokens Ternary Bonsai 1.7B: $0.00 input / $0.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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