Model comparison
GLM-5 vs Sarvam 30B
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-5 #28 (Supported); Sarvam 30B #169 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and Sarvam 30B share 11 comparable benchmark results. 0 of 8 categories are comparable. 38 results are unique to GLM-5; 0 to Sarvam 30B.
Updated July 18, 2026- Shared results
- 11
- GLM-5 only
- 38
- Sarvam 30B only
- 0
- Comparable categories
- 0 / 8
Benchmark data for GLM-5 and Sarvam 30B 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-5 is priced at $1.00 input / $3.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Sarvam 30B. GLM-5 has the larger context window at 200K, compared with 64K for Sarvam 30B.
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-5 | Δ | Sarvam 30B |
|---|---|---|---|
| Agentic | GLM-556.2 | MarginNo overlap | Sarvam 30BNot measured |
| Coding | GLM-566.3 | MarginNo overlap | Sarvam 30BNot measured |
| Reasoning | GLM-560.8 | MarginNo overlap | Sarvam 30BNot measured |
| Knowledge | GLM-566.4 | MarginNo overlap | Sarvam 30BNot measured |
| Math | GLM-556.3 | MarginNo overlap | Sarvam 30BNot measured |
| Multilingual | GLM-583.1 | MarginNo overlap | Sarvam 30BNot measured |
| Inst. Following | GLM-592.6 | MarginNo overlap | Sarvam 30BNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5 | Sarvam 30B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | Sarvam 30B$0 input / $0 output | Sarvam 30B has the lower combined listed price. |
| Generation speedtokens per second | GLM-574 tok/s | Sarvam 30BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-51.64 s | Sarvam 30BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5200K | Sarvam 30B64K | GLM-5 lists the larger context window. |
Benchmark Deep Dive
Agentic13 benchmarks
| Benchmark | GLM-5 | Sarvam 30B | 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% | 34.5% | GLM-5 leads |
| CyberGymSource | 43.2% | — | Not comparable |
| APEX-Agents-AASource | 14.5% | — | Not comparable |
| Gert LabsSource | 50.99% | — | Not comparable |
Coding7 benchmarks
| Benchmark | GLM-5 | Sarvam 30B | 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 |
| AA-SciCodeSource | 46.2% | 19.2% | GLM-5 leads |
Reasoning4 benchmarks
Knowledge12 benchmarks
| Benchmark | GLM-5 | Sarvam 30B | 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% | 6.6% | GLM-5 leads |
| AA-GPQA DiamondSource | 82.0% | 63.3% | GLM-5 leads |
| AA-HLESource | 27.2% | 7.0% | GLM-5 leads |
| AA-Omniscience IndexSource | 2.0% | -72.0% | GLM-5 leads |
| AA-Omniscience AccuracySource | 26.9% | 12.7% | GLM-5 leads |
| AA-Omniscience Hallucination RateSource | 34.0% | 97.0% | GLM-5 leads |
Math8 benchmarks
| Benchmark | GLM-5 | Sarvam 30B | 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 | GLM-5 | Sarvam 30B | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1280 | — | Not comparable |
Frequently Asked Questions (3)
Can I compare GLM-5 and Sarvam 30B 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-5 and Sarvam 30B today?
GLM-5: $1.00 input / $3.20 output per 1M tokens Sarvam 30B: $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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