Model comparison
1-bit Bonsai 4B vs GLM-4.7
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use BenchLM's provisional ranking lane.
Verified leaderboard positions: 1-bit Bonsai 4B unranked; GLM-4.7 #32
Evidence parity. 1-bit Bonsai 4B and GLM-4.7 share 0 comparable benchmark results. 0 of 8 categories are comparable. 0 results are unique to 1-bit Bonsai 4B; 31 to GLM-4.7.
Updated July 14, 2026- Shared results
- 0
- 1-bit Bonsai 4B only
- 0
- GLM-4.7 only
- 31
- Comparable categories
- 0 / 8
Benchmark data for 1-bit Bonsai 4B and GLM-4.7 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 4B yet. This comparison is currently limited to metadata such as context window, reasoning mode, and pricing where available.
GLM-4.7 has the larger context window at 200K, compared with 32K for 1-bit Bonsai 4B.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | 1-bit Bonsai 4B | GLM-4.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | 1-bit Bonsai 4B$0 input / $0 output | GLM-4.7$0 input / $0 output | Listed prices are equal. |
| Generation speedtokens per second | 1-bit Bonsai 4BNot available | GLM-4.782 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | 1-bit Bonsai 4BNot available | GLM-4.71.10 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | 1-bit Bonsai 4B32K | GLM-4.7200K | GLM-4.7 lists the larger context window. |
Benchmark Deep Dive
Agentic8 benchmarks
| Benchmark | 1-bit Bonsai 4B | GLM-4.7 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | — | 41% | Not comparable |
| BrowseCompSource | — | 52% | Not comparable |
| VITA-BenchSource | — | 15.5% | Not comparable |
| AA Agentic IndexSource | — | 25.4% | Not comparable |
| Tau2-TelecomSource | — | 95.9% | Not comparable |
| Gert LabsSource | — | 39.95% | Not comparable |
| GDPval-AASource | — | 33.3% | Not comparable |
| GDPval-AASource | — | 1165 | Not comparable |
Coding7 benchmarks
| Benchmark | 1-bit Bonsai 4B | GLM-4.7 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | — | 73.8% | Not comparable |
| LiveCodeBenchSource | — | 84.9% | Not comparable |
| SWE-RebenchSource | — | 58.7% | Not comparable |
| AA Coding IndexSource | — | 45.3% | Not comparable |
| Terminal-Bench HardSource | — | 31.8% | Not comparable |
| AA-SciCodeSource | — | 45.1% | Not comparable |
| AA LiveCodeBenchSource | — | 89.4% | Not comparable |
Reasoning2 benchmarks
Knowledge9 benchmarks
| Benchmark | 1-bit Bonsai 4B | GLM-4.7 | Result |
|---|---|---|---|
| GPQASource | — | 85.7% | Not comparable |
| MMLU-ProSource | — | 84.3% | Not comparable |
| HLESource | — | 24.8% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 33.7% | Not comparable |
| AA-GPQA DiamondSource | — | 85.9% | Not comparable |
| AA-HLESource | — | 25.1% | Not comparable |
| AA-Omniscience IndexSource | — | -34.6% | Not comparable |
| AA-Omniscience AccuracySource | — | 29.3% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 90.3% | Not comparable |
Math3 benchmarks
Multimodal1 benchmarks
| Benchmark | 1-bit Bonsai 4B | GLM-4.7 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | — | 1260 | Not comparable |
Inst. Following1 benchmarks
| Benchmark | 1-bit Bonsai 4B | GLM-4.7 | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 67.9% | Not comparable |
Frequently Asked Questions (3)
Can I compare 1-bit Bonsai 4B and GLM-4.7 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 4B and GLM-4.7 today?
1-bit Bonsai 4B: $0.00 input / $0.00 output per 1M tokens GLM-4.7: $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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