Model comparison
1-bit Bonsai 1.7B vs MiniMax M3
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: 1-bit Bonsai 1.7B unranked (Not scored); MiniMax M3 #15 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. 1-bit Bonsai 1.7B and MiniMax M3 share 0 comparable benchmark results. 0 of 8 categories are comparable. 0 results are unique to 1-bit Bonsai 1.7B; 45 to MiniMax M3.
Updated July 21, 2026- Shared results
- 0
- 1-bit Bonsai 1.7B only
- 0
- MiniMax M3 only
- 45
- Comparable categories
- 0 / 8
Benchmark data for 1-bit Bonsai 1.7B and MiniMax M3 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 1-bit Bonsai 1.7B yet. This comparison is currently limited to metadata such as context window, reasoning mode, and pricing where available.
MiniMax M3 is priced at $0.30 input / $1.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for 1-bit Bonsai 1.7B. MiniMax M3 has the larger context window at 1M, compared with 32K for 1-bit Bonsai 1.7B.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | 1-bit Bonsai 1.7B | MiniMax M3 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | 1-bit Bonsai 1.7B$0 input / $0 output | MiniMax M3$0.3 input / $1.2 output | 1-bit Bonsai 1.7B has the lower combined listed price. |
| Generation speedtokens per second | 1-bit Bonsai 1.7BNot available | MiniMax M3Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | 1-bit Bonsai 1.7BNot available | MiniMax M3Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | 1-bit Bonsai 1.7B32K | MiniMax M31M | MiniMax M3 lists the larger context window. |
Benchmark Deep Dive
Agentic18 benchmarks
| Benchmark | 1-bit Bonsai 1.7B | MiniMax M3 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | — | 66% | Not comparable |
| BrowseCompSource | — | 83.5% | Not comparable |
| OSWorld-VerifiedSource | — | 70.1% | Not comparable |
| MCP AtlasSource | — | 74.2% | Not comparable |
| Claw-EvalSource | — | 74.5% | Not comparable |
| AA Agentic IndexSource | — | 35.4% | Not comparable |
| τ²-bench resultsSource | — | 88.9% | Not comparable |
| GDPval-AASource | — | 44.7% | Not comparable |
| GDPval-AASource | — | 1395 | Not comparable |
| GDPval rubricsSource | — | 74.7% | Not comparable |
| BankerToolBenchSource | — | 76.1% | Not comparable |
| ResearchClawBenchSource | — | 19.8% | Not comparable |
| OSWorld 2.0Source | — | 4.6% | Not comparable |
| AA BriefcaseSource | — | 1110 | Not comparable |
| AA EnterpriseOps-GymSource | — | 32.1% | Not comparable |
| AA Harvey LABSource | — | 88.4% | Not comparable |
| terminalBenchHardSource | — | 42.4% | Not comparable |
| aaTerminalBench21Source | — | 65.2% | Not comparable |
Coding9 benchmarks
| Benchmark | 1-bit Bonsai 1.7B | MiniMax M3 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | — | 80.5% | Not comparable |
| SWE-bench ProSource | — | 59% | Not comparable |
| Terminal-Bench 2.0Source | — | 66.0% | Not comparable |
| NL2RepoSource | — | 42.1% | Not comparable |
| AA Coding IndexSource | — | 58.6% | Not comparable |
| AA-SciCodeSource | — | 45.4% | Not comparable |
| VIBE V2Source | — | 50.1% | Not comparable |
| SVG-BenchSource | — | 63.7% | Not comparable |
| KernelBench HardSource | — | 28.8% | Not comparable |
Reasoning2 benchmarks
Knowledge7 benchmarks
| Benchmark | 1-bit Bonsai 1.7B | MiniMax M3 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | — | 44.4% | Not comparable |
| AA-GPQA DiamondSource | — | 92.9% | Not comparable |
| AA-HLESource | — | 37.1% | Not comparable |
| AA-Omniscience IndexSource | — | 1.4% | Not comparable |
| AA-Omniscience AccuracySource | — | 15.0% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 16.1% | Not comparable |
| AA Openness IndexSource | — | 33.3% | Not comparable |
Math1 benchmarks
| Benchmark | 1-bit Bonsai 1.7B | MiniMax M3 | Result |
|---|---|---|---|
| USAMO 2026Source | — | 85.7% | Not comparable |
Multimodal7 benchmarks
| Benchmark | 1-bit Bonsai 1.7B | MiniMax M3 | Result |
|---|---|---|---|
| OfficeQA ProSource | — | 45.1% | Not comparable |
| OmniDocBench 1.5Source | — | 91.6% | Not comparable |
| MMMU-ProSource | — | 78.1% | Not comparable |
| VideoMMMUSource | — | 84.6% | Not comparable |
| Video-MME (with subtitle)Source | — | 85.4% | Not comparable |
| Design Arena WebsiteSource | — | 1289 | Not comparable |
| AA-MMMU-ProSource | — | 78.6% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | 1-bit Bonsai 1.7B | MiniMax M3 | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 82.9% | Not comparable |
Frequently Asked Questions (3)
Can I compare 1-bit Bonsai 1.7B and MiniMax M3 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 1-bit Bonsai 1.7B and MiniMax M3 today?
1-bit Bonsai 1.7B: $0.00 input / $0.00 output per 1M tokens MiniMax M3: $0.30 input / $1.20 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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