Model comparison
GLM-5 vs Interfaze Beta
Head-to-head evidence from 2 shared benchmark results across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5 #28 (Supported); Interfaze Beta unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and Interfaze Beta share 2 comparable benchmark results. 1 of 8 categories are comparable. 47 results are unique to GLM-5; 8 to Interfaze Beta.
Updated July 18, 2026- Shared results
- 2
- GLM-5 only
- 47
- Interfaze Beta only
- 8
- Comparable categories
- 1 / 8
Treat this as a split decision. GLM-5 makes more sense if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model; Interfaze Beta is the better fit if knowledge is the priority or you need the larger 1M context window.
Confidence note. This is a partial-evidence comparison with 2 shared benchmark results across 1 evidence category; 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 Interfaze Beta 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.
Interfaze Beta is also the more expensive model on tokens at $1.50 input / $3.50 output per 1M tokens, versus $1.00 input / $3.20 output per 1M tokens for GLM-5. Interfaze Beta is the reasoning model in the pair, while GLM-5 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Interfaze Beta gives you the larger context window at 1M, compared with 200K for GLM-5.
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 | Interfaze Beta |
|---|---|---|---|
| Knowledge | GLM-566.4 | Margin→ 23.5 | Interfaze Beta89.9 |
| Agentic | GLM-556.2 | MarginNo overlap | Interfaze BetaNot measured |
| Coding | GLM-566.3 | MarginNo overlap | Interfaze BetaNot measured |
| Reasoning | GLM-560.8 | MarginNo overlap | Interfaze BetaNot measured |
| Math | GLM-556.3 | MarginNo overlap | Interfaze BetaNot measured |
| Multilingual | GLM-583.1 | MarginNo overlap | Interfaze BetaNot measured |
| Multimodal | GLM-5Not measured | MarginNo overlap | Interfaze Beta71.1 |
| Inst. Following | GLM-592.6 | MarginNo overlap | Interfaze BetaNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 86%B 89.9%Winner: Interfaze BetaΔ 3.9GPQA: GLM-5 scored 86%; Interfaze Beta scored 89.9%. Interfaze Beta wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5 | Interfaze Beta | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | Interfaze Beta$1.5 input / $3.5 output | GLM-5 has the lower combined listed price. |
| Generation speedtokens per second | GLM-574 tok/s | Interfaze BetaNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-51.64 s | Interfaze BetaNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5200K | Interfaze Beta1M | Interfaze Beta lists the larger context window. |
Benchmark Deep Dive
Agentic13 benchmarks
| Benchmark | GLM-5 | Interfaze Beta | 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 | GLM-5 | Interfaze Beta | 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 |
| Spider 2.0-LiteSource | — | 52.9% | Not comparable |
Reasoning4 benchmarks
KnowledgeInterfaze Beta wins13 benchmarks
| Benchmark | GLM-5 | Interfaze Beta | Result |
|---|---|---|---|
| GPQASource | 86% | 89.9% | Interfaze Beta leads |
| GPQA-DSource | 86.0% | 89.9% | Interfaze Beta leads |
| 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 |
| MMMLUSource | — | 90.9% | Not comparable |
Math8 benchmarks
| Benchmark | GLM-5 | Interfaze Beta | 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
Multimodal6 benchmarks
Frequently Asked Questions (2)
Which is better, GLM-5 or Interfaze Beta?
GLM-5 and Interfaze Beta 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 knowledge tasks, GLM-5 or Interfaze Beta?
Interfaze Beta has the edge for knowledge tasks in this comparison, averaging 89.9 versus 66.4. Inside this category, GPQA is the benchmark that creates the most daylight between them.
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