Model comparison
Gemini 3.1 Pro 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.
BenchAlign evidence: Gemini 3.1 Pro estimated; Ternary Bonsai 1.7B not scored. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Gemini 3.1 Pro and Ternary Bonsai 1.7B share 0 comparable benchmark results. 0 of 8 categories are comparable. 48 results are unique to Gemini 3.1 Pro; 0 to Ternary Bonsai 1.7B.
Updated July 16, 2026- Shared results
- 0
- Gemini 3.1 Pro only
- 48
- Ternary Bonsai 1.7B only
- 0
- Comparable categories
- 0 / 8
Benchmark data for Gemini 3.1 Pro 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.
Gemini 3.1 Pro is priced at $2.00 input / $12.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Ternary Bonsai 1.7B. Gemini 3.1 Pro has the larger context window at 1M, 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 | Gemini 3.1 Pro | Ternary Bonsai 1.7B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Gemini 3.1 Pro$2 input / $12 output | Ternary Bonsai 1.7B$0 input / $0 output | Ternary Bonsai 1.7B has the lower combined listed price. |
| Generation speedtokens per second | Gemini 3.1 Pro109 tok/s | Ternary Bonsai 1.7BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Gemini 3.1 Pro29.71 s | Ternary Bonsai 1.7BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Gemini 3.1 Pro1M | Ternary Bonsai 1.7B32K | Gemini 3.1 Pro lists the larger context window. |
Benchmark Deep Dive
Agentic14 benchmarks
| Benchmark | Gemini 3.1 Pro | Ternary Bonsai 1.7B | Result |
|---|---|---|---|
| Claw-EvalSource | 57.8% | — | Not comparable |
| DeepSearchQASource | 69.7% | — | Not comparable |
| τ²-bench resultsSource | 95.6% | — | Not comparable |
| AA Agentic IndexSource | 21.4% | — | Not comparable |
| APEX-Agents-AASource | 32.0% | — | Not comparable |
| GDPval-AASource | 23.1% | — | Not comparable |
| GDPval-AASource | 962 | — | Not comparable |
| Gert LabsSource | 49.91% | — | Not comparable |
| ResearchClawBenchSource | 13.3% | — | Not comparable |
| AA AutomationBenchSource | 37.5% | — | Not comparable |
| AA EnterpriseOps-GymSource | 42.2% | — | Not comparable |
| AA Harvey LABSource | 0.0% | — | Not comparable |
| AA ITBenchSource | 30.3% | — | Not comparable |
| AA Tau3 BankingSource | 16.5% | — | Not comparable |
Coding7 benchmarks
| Benchmark | Gemini 3.1 Pro | Ternary Bonsai 1.7B | Result |
|---|---|---|---|
| LiveCodeBench ProSource | 82.9% | — | Not comparable |
| React Native EvalsSource | 78.9% | — | Not comparable |
| Vibe Code BenchSource | 32.03% | — | Not comparable |
| AA Coding IndexSource | 68.8% | — | Not comparable |
| Terminal-Bench HardSource | 53.8% | — | Not comparable |
| AA-SciCodeSource | 58.9% | — | Not comparable |
| AA Terminal-Bench 2.1Source | 73.8% | — | Not comparable |
Reasoning3 benchmarks
Knowledge10 benchmarks
| Benchmark | Gemini 3.1 Pro | Ternary Bonsai 1.7B | Result |
|---|---|---|---|
| GPQA-DSource | 94.3% | — | Not comparable |
| HLE w/o toolsSource | 45.4% | — | Not comparable |
| HealthBench HardSource | 20.6% | — | Not comparable |
| MedXpertQA (Text)Source | 71.5% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 46.5% | — | Not comparable |
| AA-GPQA DiamondSource | 94.1% | — | Not comparable |
| AA-HLESource | 44.7% | — | Not comparable |
| AA-Omniscience IndexSource | 32.9% | — | Not comparable |
| AA-Omniscience AccuracySource | 55.3% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 49.9% | — | Not comparable |
Math2 benchmarks
Multilingual1 benchmarks
| Benchmark | Gemini 3.1 Pro | Ternary Bonsai 1.7B | Result |
|---|---|---|---|
| AA Global-MMLU-LiteSource | 93.2% | — | Not comparable |
Multimodal10 benchmarks
| Benchmark | Gemini 3.1 Pro | Ternary Bonsai 1.7B | Result |
|---|---|---|---|
| MMMU-ProSource | 83.9% | — | Not comparable |
| CharXivSource | 80.2% | — | Not comparable |
| ERQASource | 69.4% | — | Not comparable |
| SimpleVQASource | 72.4% | — | Not comparable |
| ScreenSpot ProSource | 84.4% | — | Not comparable |
| ZeroBenchSource | 29.0% | — | Not comparable |
| MedXpertQA (MM)Source | 81.3% | — | Not comparable |
| GDPval-AASource | 1320 | — | Not comparable |
| AA-MMMU-ProSource | 82.4% | — | Not comparable |
| Design Arena WebsiteSource | 1285 | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Gemini 3.1 Pro | Ternary Bonsai 1.7B | Result |
|---|---|---|---|
| AA-IFBenchSource | 77.1% | — | Not comparable |
Frequently Asked Questions (3)
Can I compare Gemini 3.1 Pro 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 Gemini 3.1 Pro and Ternary Bonsai 1.7B today?
Gemini 3.1 Pro: $2.00 input / $12.00 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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