Model comparison
1-bit Bonsai 1.7B vs GLM-5
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: 1-bit Bonsai 1.7B unranked; GLM-5 #15
BenchAlign evidence: 1-bit Bonsai 1.7B not scored; GLM-5 supported. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. 1-bit Bonsai 1.7B and GLM-5 share 0 comparable benchmark results. 0 of 8 categories are comparable. 0 results are unique to 1-bit Bonsai 1.7B; 50 to GLM-5.
Updated July 16, 2026- Shared results
- 0
- 1-bit Bonsai 1.7B only
- 0
- GLM-5 only
- 50
- Comparable categories
- 0 / 8
Benchmark data for 1-bit Bonsai 1.7B and GLM-5 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.
GLM-5 is priced at $1.00 input / $3.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for 1-bit Bonsai 1.7B. GLM-5 has the larger context window at 200K, 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 | GLM-5 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | 1-bit Bonsai 1.7B$0 input / $0 output | GLM-5$1 input / $3.2 output | 1-bit Bonsai 1.7B has the lower combined listed price. |
| Generation speedtokens per second | 1-bit Bonsai 1.7BNot available | GLM-574 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | 1-bit Bonsai 1.7BNot available | GLM-51.64 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | 1-bit Bonsai 1.7B32K | GLM-5200K | GLM-5 lists the larger context window. |
Benchmark Deep Dive
Agentic13 benchmarks
| Benchmark | 1-bit Bonsai 1.7B | GLM-5 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | — | 56.2% | Not comparable |
| Claw-EvalSource | — | 57.7% | Not comparable |
| QwenClawBenchSource | — | 54.1% | Not comparable |
| τ³-bench resultsSource | — | 65.6% | Not comparable |
| DeepPlanningSource | — | 14.6% | Not comparable |
| ToolathlonSource | — | 38% | Not comparable |
| MCP AtlasSource | — | 31.1% | Not comparable |
| MCP-TasksSource | — | 60.8% | Not comparable |
| WideResearchSource | — | 69.8% | Not comparable |
| τ²-bench resultsSource | — | 98.2% | Not comparable |
| CyberGymSource | — | 43.2% | Not comparable |
| APEX-Agents-AASource | — | 14.5% | Not comparable |
| Gert LabsSource | — | 50.99% | Not comparable |
Coding8 benchmarks
| Benchmark | 1-bit Bonsai 1.7B | GLM-5 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | — | 77.8% | Not comparable |
| SWE-bench Verified*Source | — | 72.8% | Not comparable |
| SWE-bench ProSource | — | 55.1% | Not comparable |
| SWE MultilingualSource | — | 73.3% | Not comparable |
| SWE-RebenchSource | — | 62.8% | Not comparable |
| React Native EvalsSource | — | 74.8% | Not comparable |
| Terminal-Bench HardSource | — | 43.2% | Not comparable |
| AA-SciCodeSource | — | 46.2% | Not comparable |
Reasoning4 benchmarks
Knowledge12 benchmarks
| Benchmark | 1-bit Bonsai 1.7B | GLM-5 | Result |
|---|---|---|---|
| GPQASource | — | 86% | Not comparable |
| GPQA-DSource | — | 86.0% | Not comparable |
| SuperGPQASource | — | 66.8% | Not comparable |
| MMLU-ProSource | — | 85.7% | Not comparable |
| MMLU-Pro (Arcee)Source | — | 85.8% | Not comparable |
| HLESource | — | 50.4% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 39.5% | Not comparable |
| AA-GPQA DiamondSource | — | 82.0% | Not comparable |
| AA-HLESource | — | 27.2% | Not comparable |
| AA-Omniscience IndexSource | — | 2.0% | Not comparable |
| AA-Omniscience AccuracySource | — | 26.9% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 34.0% | Not comparable |
Math8 benchmarks
| Benchmark | 1-bit Bonsai 1.7B | GLM-5 | Result |
|---|---|---|---|
| AIME26Source | — | 95.8% | Not comparable |
| AIME25 (Arcee)Source | — | 93.3% | Not comparable |
| HMMT Feb 2025Source | — | 97.5% | Not comparable |
| HMMT Nov 2025Source | — | 96.9% | Not comparable |
| HMMT Feb 2026Source | — | 86.4% | Not comparable |
| MMAnswerBenchSource | — | 82.5% | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | — | 16.434% | Not comparable |
| FrontierMath v2 (Tier 4)Source | — | 2.100% | Not comparable |
Multilingual2 benchmarks
Multimodal1 benchmarks
| Benchmark | 1-bit Bonsai 1.7B | GLM-5 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | — | 1282 | Not comparable |
Frequently Asked Questions (3)
Can I compare 1-bit Bonsai 1.7B and GLM-5 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 GLM-5 today?
1-bit Bonsai 1.7B: $0.00 input / $0.00 output per 1M tokens GLM-5: $1.00 input / $3.20 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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