Model comparison
GLM-5.1 vs GPT-5.6 Luna
Head-to-head evidence from 20 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5.1 #18 (Supported); GPT-5.6 Luna #22 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5.1 and GPT-5.6 Luna share 20 comparable benchmark results. 4 of 8 categories are comparable. 16 results are unique to GLM-5.1; 21 to GPT-5.6 Luna.
Updated July 23, 2026- Shared results
- 20
- GLM-5.1 only
- 16
- GPT-5.6 Luna only
- 21
- Comparable categories
- 4 / 8
Pick GLM-5.1 if you want the stronger benchmark profile. GPT-5.6 Luna only becomes the better choice if knowledge is the priority or you need the larger 1M context window.
Confidence note. This is a partial-evidence comparison with 20 shared benchmark results across 5 evidence categories; 4 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GLM-5.1 has the cleaner BenchAlign overall profile here, landing at 67.74 versus 67.17. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
GPT-5.6 Luna is also the more expensive model on tokens at $1.00 input / $6.00 output per 1M tokens, versus $1.40 input / $4.40 output per 1M tokens for GLM-5.1. GPT-5.6 Luna gives you the larger context window at 1M, compared with 203K for GLM-5.1.
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-5.1 | Δ | GPT-5.6 Luna |
|---|---|---|---|
| Knowledge | GLM-5.152.3 | Margin→ 40.0 | GPT-5.6 Luna92.3 |
| Agentic | GLM-5.165.4 | Margin→ 18.7 | GPT-5.6 Luna84.1 |
| Math | GLM-5.162.0 | Margin→ 11.6 | GPT-5.6 Luna73.6 |
| Coding | GLM-5.161.3 | Margin→ 1.4 | GPT-5.6 Luna62.7 |
| Multimodal | GLM-5.1Not measured | MarginNo overlap | GPT-5.6 Luna78.4 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
FrontierMath v2 (Tier 4)
MathA 12.500%B 58.500%Winner: GPT-5.6 LunaΔ 46FrontierMath v2 (Tier 4): GLM-5.1 scored 12.500%; GPT-5.6 Luna scored 58.500%. GPT-5.6 Luna wins this benchmark. - Source ↗
FrontierMath v2 (Tiers 1-3)
MathA 33.448%B 78.600%Winner: GPT-5.6 LunaΔ 45.2FrontierMath v2 (Tiers 1-3): GLM-5.1 scored 33.448%; GPT-5.6 Luna scored 78.600%. GPT-5.6 Luna wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 63.5%B 84.7%Winner: GPT-5.6 LunaΔ 21.2Terminal-Bench 2.0: GLM-5.1 scored 63.5%; GPT-5.6 Luna scored 84.7%. GPT-5.6 Luna wins this benchmark. - Source ↗
BrowseComp
AgenticA 68%B 83.3%Winner: GPT-5.6 LunaΔ 15.3BrowseComp: GLM-5.1 scored 68%; GPT-5.6 Luna scored 83.3%. GPT-5.6 Luna wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 58.4%B 62.7%Winner: GPT-5.6 LunaΔ 4.3SWE-bench Pro: GLM-5.1 scored 58.4%; GPT-5.6 Luna scored 62.7%. GPT-5.6 Luna wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5.1 | GPT-5.6 Luna | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5.1$1.4 input / $4.4 output | GPT-5.6 Luna$1 input / $6 output | GLM-5.1 has the lower combined listed price. |
| Generation speedtokens per second | GLM-5.1Not available | GPT-5.6 LunaNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-5.1Not available | GPT-5.6 LunaNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5.1203K | GPT-5.6 Luna1M | GPT-5.6 Luna lists the larger context window. |
Benchmark Deep Dive
AgenticGPT-5.6 Luna wins21 benchmarks
| Benchmark | GLM-5.1 | GPT-5.6 Luna | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 63.5% | 84.7% | GPT-5.6 Luna leads |
| BrowseCompSource | 68% | 83.3% | GPT-5.6 Luna leads |
| τ³-bench resultsSource | 70.6% | — | Not comparable |
| MCP AtlasSource | 71.8% | — | Not comparable |
| CyberGymSource | 68.7% | 77.9% | GPT-5.6 Luna leads |
| Claw-EvalSource | 62.3% | — | Not comparable |
| AA Agentic IndexSource | 29.9% | 45.6% | GPT-5.6 Luna leads |
| τ²-bench resultsSource | 97.7% | — | Not comparable |
| GDPval-AASource | 37.8% | 54.2% | GPT-5.6 Luna leads |
| Gert LabsSource | 60.11% | — | Not comparable |
| GDPval-AASource | 1257 | 1584 | GPT-5.6 Luna leads |
| ResearchClawBenchSource | 18.2% | — | Not comparable |
| OSWorld 2.0Source | — | 45.6% | Not comparable |
| ExploitGymSource | — | 12.4% | Not comparable |
| ToolathlonSource | — | 53.4% | Not comparable |
| AA Harvey LABSource | — | 87.9% | Not comparable |
| AA ITBenchSource | — | 40.3% | Not comparable |
| AA Tau3 BankingSource | — | 27.2% | Not comparable |
| AA AutomationBenchSource | — | 42.2% | Not comparable |
| aaTerminalBench21Source | — | 80.9% | Not comparable |
| APEX-Agents-AASource | — | 35.8% | Not comparable |
CodingGPT-5.6 Luna wins10 benchmarks
| Benchmark | GLM-5.1 | GPT-5.6 Luna | Result |
|---|---|---|---|
| SWE-bench ProSource | 58.4% | 62.7% | GPT-5.6 Luna leads |
| NL2RepoSource | 42.7% | — | Not comparable |
| SWE-RebenchSource | 62.7% | — | Not comparable |
| Vibe Code BenchSource | 31.46% | — | Not comparable |
| AA Coding IndexSource | 55.8% | 71.5% | GPT-5.6 Luna leads |
| AA-SciCodeSource | 43.8% | 52.5% | GPT-5.6 Luna leads |
| Terminal-Bench 2.0Source | — | 84.7% | Not comparable |
| deepSweSource | — | 67.2% | Not comparable |
| FrontierCode 1.1 ExtendedSource | — | 55.1% | Not comparable |
| cursorBench32Source | — | 61.1% | Not comparable |
Reasoning3 benchmarks
KnowledgeGPT-5.6 Luna wins11 benchmarks
| Benchmark | GLM-5.1 | GPT-5.6 Luna | Result |
|---|---|---|---|
| GPQA-DSource | 86.2% | 92.3% | GPT-5.6 Luna leads |
| HLESource | 52.3% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 40.2% | 51.2% | GPT-5.6 Luna leads |
| AA-GPQA DiamondSource | 86.8% | 91.1% | GPT-5.6 Luna leads |
| AA-HLESource | 28.0% | 37.2% | GPT-5.6 Luna leads |
| AA-Omniscience IndexSource | 1.9% | -11.2% | GLM-5.1 leads |
| AA-Omniscience AccuracySource | 24.2% | 41.5% | GPT-5.6 Luna leads |
| AA-Omniscience Hallucination RateSource | 29.4% | 90.1% | GLM-5.1 leads |
| GPQASource | — | 92.3% | Not comparable |
| HealthBench ProfessionalSource | — | 55.7% | Not comparable |
| HealthBench HardSource | — | 32.0% | Not comparable |
MathGPT-5.6 Luna wins7 benchmarks
| Benchmark | GLM-5.1 | GPT-5.6 Luna | Result |
|---|---|---|---|
| AIME26Source | 95.3% | — | Not comparable |
| HMMT Nov 2025Source | 94.0% | — | Not comparable |
| HMMT Feb 2026Source | 82.6% | — | Not comparable |
| MMAnswerBenchSource | 83.8% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 33.448% | 78.600% | GPT-5.6 Luna leads |
| FrontierMath v2 (Tier 4)Source | 12.500% | 58.500% | GPT-5.6 Luna leads |
| FrontierMath (legacy)Source | — | 78.6% | Not comparable |
Multimodal4 benchmarks
Inst. Following1 benchmarks
| Benchmark | GLM-5.1 | GPT-5.6 Luna | Result |
|---|---|---|---|
| AA-IFBenchSource | 76.3% | — | Not comparable |
Frequently Asked Questions (5)
Which is better, GLM-5.1 or GPT-5.6 Luna?
GLM-5.1 is ahead on BenchLM's BenchAlign leaderboard, 67.74 to 67.17. The biggest single separator in this matchup is FrontierMath v2 (Tier 4), where the scores are 12.500% and 58.500%.
Which is better for knowledge tasks, GLM-5.1 or GPT-5.6 Luna?
GPT-5.6 Luna has the edge for knowledge tasks in this comparison, averaging 92.3 versus 52.3. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Which is better for coding, GLM-5.1 or GPT-5.6 Luna?
GPT-5.6 Luna has the edge for coding in this comparison, averaging 62.7 versus 61.3. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
Which is better for math, GLM-5.1 or GPT-5.6 Luna?
GPT-5.6 Luna has the edge for math in this comparison, averaging 73.6 versus 62. Inside this category, FrontierMath v2 (Tier 4) is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GLM-5.1 or GPT-5.6 Luna?
GPT-5.6 Luna has the edge for agentic tasks in this comparison, averaging 84.1 versus 65.4. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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