Model comparison
GLM-5 vs Holo3-122B-A10B
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: GLM-5 #28 (Supported); Holo3-122B-A10B unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and Holo3-122B-A10B share 0 comparable benchmark results. 1 of 8 categories are comparable. 49 results are unique to GLM-5; 1 to Holo3-122B-A10B.
Updated July 23, 2026- Shared results
- 0
- GLM-5 only
- 49
- Holo3-122B-A10B only
- 1
- Comparable categories
- 1 / 8
Treat this as a split decision. GLM-5 makes more sense if you need the larger 200K context window; Holo3-122B-A10B is the better fit if agentic is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 evidence categories; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GLM-5 and Holo3-122B-A10B finish on the same BenchAlign overall score, so this is less about a single winner and more about where the edge shows up. The BenchAlign headline says tie; the benchmark table is where the real choice happens.
GLM-5 is also the more expensive model on tokens at $1.00 input / $3.20 output per 1M tokens, versus $0.40 input / $3.00 output per 1M tokens for Holo3-122B-A10B. GLM-5 gives you the larger context window at 200K, compared with 64K for Holo3-122B-A10B.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5 | Holo3-122B-A10B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | Holo3-122B-A10B$0.4 input / $3 output | Holo3-122B-A10B has the lower combined listed price. |
| Generation speedtokens per second | GLM-574 tok/s | Holo3-122B-A10BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-51.64 s | Holo3-122B-A10BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5200K | Holo3-122B-A10B64K | GLM-5 lists the larger context window. |
Benchmark Deep Dive
AgenticHolo3-122B-A10B wins14 benchmarks
| Benchmark | GLM-5 | Holo3-122B-A10B | 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 |
| OSWorld-VerifiedSource | — | 78.8% | Not comparable |
Coding7 benchmarks
| Benchmark | GLM-5 | Holo3-122B-A10B | 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% | — | Not comparable |
Reasoning4 benchmarks
Knowledge12 benchmarks
| Benchmark | GLM-5 | Holo3-122B-A10B | 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 | GLM-5 | Holo3-122B-A10B | 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 | Holo3-122B-A10B | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1278 | — | Not comparable |
Frequently Asked Questions (2)
Which is better, GLM-5 or Holo3-122B-A10B?
GLM-5 and Holo3-122B-A10B are tied on the BenchAlign overall score, so the right pick depends on which category matters most for your use case.
Which is better for agentic tasks, GLM-5 or Holo3-122B-A10B?
Holo3-122B-A10B has the edge for agentic tasks in this comparison, averaging 78.9 versus 56.2. GLM-5 stays close enough that the answer can still flip depending on your workload.
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