Model comparison
GPT-5.4 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.
Verified leaderboard positions: GPT-5.4 #10; Ternary Bonsai 1.7B unranked
BenchAlign evidence: GPT-5.4 supported; Ternary Bonsai 1.7B not scored. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.4 and Ternary Bonsai 1.7B share 0 comparable benchmark results. 0 of 8 categories are comparable. 54 results are unique to GPT-5.4; 0 to Ternary Bonsai 1.7B.
Updated July 16, 2026- Shared results
- 0
- GPT-5.4 only
- 54
- Ternary Bonsai 1.7B only
- 0
- Comparable categories
- 0 / 8
Benchmark data for GPT-5.4 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.
GPT-5.4 is priced at $2.50 input / $15.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Ternary Bonsai 1.7B. GPT-5.4 has the larger context window at 1.05M, 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 | GPT-5.4 | Ternary Bonsai 1.7B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.4$2.5 input / $15 output | Ternary Bonsai 1.7B$0 input / $0 output | Ternary Bonsai 1.7B has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.474 tok/s | Ternary Bonsai 1.7BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.4151.79 s | Ternary Bonsai 1.7BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.41.05M | Ternary Bonsai 1.7B32K | GPT-5.4 lists the larger context window. |
Benchmark Deep Dive
Agentic17 benchmarks
| Benchmark | GPT-5.4 | Ternary Bonsai 1.7B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 75.1% | — | Not comparable |
| CyberGymSource | 79.0% | — | Not comparable |
| BrowseCompSource | 82.7% | — | Not comparable |
| OSWorld-VerifiedSource | 75% | — | Not comparable |
| MCP AtlasSource | 70.6% | — | Not comparable |
| ToolathlonSource | 54.6% | — | Not comparable |
| τ²-bench resultsSource | 98.9% | — | Not comparable |
| Claw-EvalSource | 60.3% | — | Not comparable |
| DeepSearchQASource | 73.6% | — | Not comparable |
| AA Agentic IndexSource | 41.1% | — | Not comparable |
| APEX-Agents-AASource | 33.3% | — | Not comparable |
| GDPval-AASource | 44.7% | — | Not comparable |
| GDPval-AASource | 1395 | — | Not comparable |
| Gert LabsSource | 64.89% | — | Not comparable |
| ResearchClawBenchSource | 15.3% | — | Not comparable |
| JobBenchSource | 38.9% | — | Not comparable |
| ExploitGymSource | 6.0% | — | Not comparable |
Coding7 benchmarks
| Benchmark | GPT-5.4 | Ternary Bonsai 1.7B | Result |
|---|---|---|---|
| LiveCodeBench ProSource | 87.5% | — | Not comparable |
| SWE-bench ProSource | 57.7% | — | Not comparable |
| React Native EvalsSource | 85.3% | — | Not comparable |
| Vibe Code BenchSource | 67.42% | — | Not comparable |
| AA Coding IndexSource | 71.0% | — | Not comparable |
| Terminal-Bench HardSource | 57.6% | — | Not comparable |
| AA-SciCodeSource | 56.6% | — | Not comparable |
Reasoning2 benchmarks
Knowledge13 benchmarks
| Benchmark | GPT-5.4 | Ternary Bonsai 1.7B | Result |
|---|---|---|---|
| GPQASource | 92.8% | — | Not comparable |
| HLESource | 52.1% | — | Not comparable |
| HLE w/o toolsSource | 39.8% | — | Not comparable |
| GPQA-DSource | 92.8% | — | Not comparable |
| HealthBench HardSource | 40.1% | — | Not comparable |
| MedXpertQA (Text)Source | 59.6% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 51.4% | — | Not comparable |
| AA-GPQA DiamondSource | 92.0% | — | Not comparable |
| AA-HLESource | 41.6% | — | Not comparable |
| AA-Omniscience IndexSource | 5.7% | — | Not comparable |
| AA-Omniscience AccuracySource | 50.0% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 88.6% | — | Not comparable |
| HealthBench ProfessionalSource | 48.1% | — | Not comparable |
Math2 benchmarks
Multimodal12 benchmarks
| Benchmark | GPT-5.4 | Ternary Bonsai 1.7B | Result |
|---|---|---|---|
| MMMU-ProSource | 81.2% | — | Not comparable |
| OfficeQA ProSource | 53.2% | — | Not comparable |
| MMMU-Pro w/ PythonSource | 82.1% | — | Not comparable |
| CharXivSource | 82.8% | — | Not comparable |
| ERQASource | 65.4% | — | Not comparable |
| SimpleVQASource | 61.1% | — | Not comparable |
| ScreenSpot ProSource | 85.4% | — | Not comparable |
| ZeroBenchSource | 41.0% | — | Not comparable |
| MedXpertQA (MM)Source | 77.1% | — | Not comparable |
| GDPval-AASource | 1672 | — | Not comparable |
| AA-MMMU-ProSource | 78.4% | — | Not comparable |
| Design Arena WebsiteSource | 1254 | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GPT-5.4 | Ternary Bonsai 1.7B | Result |
|---|---|---|---|
| AA-IFBenchSource | 73.9% | — | Not comparable |
Frequently Asked Questions (3)
Can I compare GPT-5.4 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 GPT-5.4 and Ternary Bonsai 1.7B today?
GPT-5.4: $2.50 input / $15.00 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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