Model comparison
Claude Mythos 5 vs GLM-5.2
Head-to-head evidence from 6 shared benchmark results across 3 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Claude Mythos 5 #1 (Supported); GLM-5.2 #37 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Claude Mythos 5 and GLM-5.2 share 6 comparable benchmark results. 4 of 8 categories are comparable. 9 results are unique to Claude Mythos 5; 37 to GLM-5.2.
Updated July 23, 2026- Shared results
- 6
- Claude Mythos 5 only
- 9
- GLM-5.2 only
- 37
- Comparable categories
- 4 / 8
Pick Claude Mythos 5 if you want the stronger benchmark profile. GLM-5.2 only becomes the better choice if you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 6 shared benchmark results across 3 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
Claude Mythos 5 is clearly ahead on the BenchAlign aggregate, 83.93 to 63.96. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Claude Mythos 5's sharpest advantage is in coding, where it averages 89.7 against 62.1. The single biggest benchmark swing on the page is SWE-bench Pro, 80.3% to 62.1%.
Claude Mythos 5 is also the more expensive model on tokens at $10.00 input / $50.00 output per 1M tokens, versus $1.40 input / $4.40 output per 1M tokens for GLM-5.2. That is roughly 11.4x on output cost alone. Claude Mythos 5 gives you the larger context window at 1M+, compared with 1M for GLM-5.2.
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 | Claude Mythos 5 | Δ | GLM-5.2 |
|---|---|---|---|
| Coding | Claude Mythos 589.7 | Margin← 27.6 | GLM-5.262.1 |
| Knowledge | Claude Mythos 568.5 | Margin← 8.9 | GLM-5.259.6 |
| Agentic | Claude Mythos 587.0 | Margin← 6.0 | GLM-5.281.0 |
| Math | Claude Mythos 597.6 | Margin← 1.7 | GLM-5.295.9 |
| Multimodal | Claude Mythos 593.5 | MarginNo overlap | GLM-5.2Not measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Pro
CodingA 80.3%B 62.1%Winner: Claude Mythos 5Δ 18.2SWE-bench Pro: Claude Mythos 5 scored 80.3%; GLM-5.2 scored 62.1%. Claude Mythos 5 wins this benchmark. - Source ↗
HLE
KnowledgeA 64.5%B 54.7%Winner: Claude Mythos 5Δ 9.8HLE: Claude Mythos 5 scored 64.5%; GLM-5.2 scored 54.7%. Claude Mythos 5 wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 88%B 81%Winner: Claude Mythos 5Δ 7Terminal-Bench 2.0: Claude Mythos 5 scored 88%; GLM-5.2 scored 81%. Claude Mythos 5 wins this benchmark. - Source ↗
GPQA
KnowledgeA 94.1%B 91.2%Winner: Claude Mythos 5Δ 2.9GPQA: Claude Mythos 5 scored 94.1%; GLM-5.2 scored 91.2%. Claude Mythos 5 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Claude Mythos 5 | GLM-5.2 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Claude Mythos 5$10 input / $50 output | GLM-5.2$1.4 input / $4.4 output | GLM-5.2 has the lower combined listed price. |
| Generation speedtokens per second | Claude Mythos 5Not available | GLM-5.2Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Claude Mythos 5Not available | GLM-5.2Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Claude Mythos 51M+ | GLM-5.21M | Claude Mythos 5 lists the larger context window. |
Benchmark Deep Dive
AgenticClaude Mythos 5 wins20 benchmarks
| Benchmark | Claude Mythos 5 | GLM-5.2 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 88% | 81% | Claude Mythos 5 leads |
| OSWorld-VerifiedSource | 85% | — | Not comparable |
| BrowseCompSource | 88% | — | Not comparable |
| ExploitGymSource | 17.5% | — | Not comparable |
| MCP AtlasSource | — | 76.8% | Not comparable |
| ToolathlonSource | — | 48.2% | Not comparable |
| AA Agentic IndexSource | — | 43.1% | Not comparable |
| τ²-bench resultsSource | — | 99.1% | Not comparable |
| GDPval-AASource | — | 50.7% | Not comparable |
| GDPval-AASource | — | 1514 | Not comparable |
| APEX-Agents-AASource | — | 33.7% | Not comparable |
| ResearchClawBenchSource | — | 20.7% | Not comparable |
| AA BriefcaseSource | — | 1260 | Not comparable |
| AA AutomationBenchSource | — | 27.8% | Not comparable |
| AA EnterpriseOps-GymSource | — | 42.7% | Not comparable |
| AA Harvey LABSource | — | 91.0% | Not comparable |
| AA ITBenchSource | — | 42.7% | Not comparable |
| AA Tau3 BankingSource | — | 26.8% | Not comparable |
| terminalBenchHardSource | — | 50.8% | Not comparable |
| aaTerminalBench21Source | — | 77.9% | Not comparable |
CodingClaude Mythos 5 wins8 benchmarks
| Benchmark | Claude Mythos 5 | GLM-5.2 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 95.5% | — | Not comparable |
| SWE-bench ProSource | 80.3% | 62.1% | Claude Mythos 5 leads |
| Terminal-Bench 2.0Source | 88.0% | 81.0% | Claude Mythos 5 leads |
| NL2RepoSource | — | 48.9% | Not comparable |
| ProgramBenchSource | — | 63.7% | Not comparable |
| cursorBench32Source | — | 55.0% | Not comparable |
| AA Coding IndexSource | — | 68.8% | Not comparable |
| AA-SciCodeSource | — | 50.5% | Not comparable |
Reasoning2 benchmarks
KnowledgeClaude Mythos 5 wins11 benchmarks
| Benchmark | Claude Mythos 5 | GLM-5.2 | Result |
|---|---|---|---|
| GPQASource | 94.1% | 91.2% | Claude Mythos 5 leads |
| HLESource | 64.5% | 54.7% | Claude Mythos 5 leads |
| HLE w/o toolsSource | 59% | 40.5% | Claude Mythos 5 leads |
| GPQA-DSource | — | 91.2% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 51.1% | Not comparable |
| AA-GPQA DiamondSource | — | 89.5% | Not comparable |
| AA-HLESource | — | 40.1% | Not comparable |
| AA-Omniscience IndexSource | — | 4.0% | Not comparable |
| AA-Omniscience AccuracySource | — | 25.1% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 28.1% | Not comparable |
| AA Openness IndexSource | — | 44.4% | Not comparable |
MathClaude Mythos 5 wins5 benchmarks
Multilingual1 benchmarks
| Benchmark | Claude Mythos 5 | GLM-5.2 | Result |
|---|---|---|---|
| SWE MultilingualSource | 92.2% | — | Not comparable |
Multimodal4 benchmarks
Inst. Following1 benchmarks
| Benchmark | Claude Mythos 5 | GLM-5.2 | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 73.3% | Not comparable |
Frequently Asked Questions (5)
Which is better, Claude Mythos 5 or GLM-5.2?
Claude Mythos 5 is ahead on BenchLM's BenchAlign leaderboard, 83.93 to 63.96. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 80.3% and 62.1%.
Which is better for knowledge tasks, Claude Mythos 5 or GLM-5.2?
Claude Mythos 5 has the edge for knowledge tasks in this comparison, averaging 68.5 versus 59.6. Inside this category, HLE w/o tools is the benchmark that creates the most daylight between them.
Which is better for coding, Claude Mythos 5 or GLM-5.2?
Claude Mythos 5 has the edge for coding in this comparison, averaging 89.7 versus 62.1. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Which is better for math, Claude Mythos 5 or GLM-5.2?
Claude Mythos 5 has the edge for math in this comparison, averaging 97.6 versus 95.9. GLM-5.2 stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, Claude Mythos 5 or GLM-5.2?
Claude Mythos 5 has the edge for agentic tasks in this comparison, averaging 87 versus 81. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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