Model comparison
GLM-4.7 vs Sarvam 105B
Head-to-head evidence from 11 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-4.7 #42 (Supported); Sarvam 105B #157 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-4.7 and Sarvam 105B share 11 comparable benchmark results. 0 of 8 categories are comparable. 19 results are unique to GLM-4.7; 0 to Sarvam 105B.
Updated July 18, 2026- Shared results
- 11
- GLM-4.7 only
- 19
- Sarvam 105B only
- 0
- Comparable categories
- 0 / 8
Benchmark data for GLM-4.7 and Sarvam 105B is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 11 shared benchmark results across 5 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 has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
GLM-4.7 has the larger context window at 200K, compared with 128K for Sarvam 105B.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | GLM-4.7 | Δ | Sarvam 105B |
|---|---|---|---|
| Agentic | GLM-4.745.7 | MarginNo overlap | Sarvam 105BNot measured |
| Coding | GLM-4.775.4 | MarginNo overlap | Sarvam 105BNot measured |
| Knowledge | GLM-4.751.8 | MarginNo overlap | Sarvam 105BNot measured |
| Math | GLM-4.71.8 | MarginNo overlap | Sarvam 105BNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-4.7 | Sarvam 105B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.7$0 input / $0 output | Sarvam 105B$0 input / $0 output | Listed prices are equal. |
| Generation speedtokens per second | GLM-4.782 tok/s | Sarvam 105BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-4.71.10 s | Sarvam 105BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-4.7200K | Sarvam 105B128K | GLM-4.7 lists the larger context window. |
Benchmark Deep Dive
Agentic8 benchmarks
| Benchmark | GLM-4.7 | Sarvam 105B | 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 |
| τ²-bench resultsSource | 95.9% | 46.8% | GLM-4.7 leads |
| Gert LabsSource | 39.95% | — | Not comparable |
| GDPval-AASource | 33.3% | — | Not comparable |
| GDPval-AASource | 1165 | — | Not comparable |
Coding6 benchmarks
Reasoning2 benchmarks
Knowledge9 benchmarks
| Benchmark | GLM-4.7 | Sarvam 105B | Result |
|---|---|---|---|
| GPQASource | 85.7% | — | Not comparable |
| MMLU-ProSource | 84.3% | — | Not comparable |
| HLESource | 24.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 33.7% | 11.9% | GLM-4.7 leads |
| AA-GPQA DiamondSource | 85.9% | 73.8% | GLM-4.7 leads |
| AA-HLESource | 25.1% | 10.1% | GLM-4.7 leads |
| AA-Omniscience IndexSource | -34.6% | -59.5% | GLM-4.7 leads |
| AA-Omniscience AccuracySource | 29.3% | 17.6% | GLM-4.7 leads |
| AA-Omniscience Hallucination RateSource | 90.3% | 93.5% | GLM-4.7 leads |
Math3 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-4.7 | Sarvam 105B | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1258 | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GLM-4.7 | Sarvam 105B | Result |
|---|---|---|---|
| AA-IFBenchSource | 67.9% | 34.4% | GLM-4.7 leads |
Frequently Asked Questions (3)
Can I compare GLM-4.7 and Sarvam 105B 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 GLM-4.7 and Sarvam 105B today?
GLM-4.7: $0.00 input / $0.00 output per 1M tokens Sarvam 105B: $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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