Model comparison
GLM-5 vs Step 3.7 Flash
Head-to-head evidence from 19 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Verified leaderboard positions: GLM-5 #15; Step 3.7 Flash unranked
BenchAlign evidence: GLM-5 supported; Step 3.7 Flash estimated. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and Step 3.7 Flash share 19 comparable benchmark results. 2 of 8 categories are comparable. 31 results are unique to GLM-5; 11 to Step 3.7 Flash.
Updated July 16, 2026- Shared results
- 19
- GLM-5 only
- 31
- Step 3.7 Flash only
- 11
- Comparable categories
- 2 / 8
Pick GLM-5 if you want the stronger benchmark profile. Step 3.7 Flash only becomes the better choice if agentic is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 19 shared benchmark results across 6 evidence categories; 2 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 is clearly ahead on the provisional aggregate, 63 to 57. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5's sharpest advantage is in coding, where it averages 66.3 against 56.3. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 56.2% to 59.5%. Step 3.7 Flash does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
GLM-5 is also the more expensive model on tokens at $1.00 input / $3.20 output per 1M tokens, versus $0.20 input / $1.15 output per 1M tokens for Step 3.7 Flash. That is roughly 2.8x on output cost alone. Step 3.7 Flash 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. Step 3.7 Flash gives you the larger context window at 256K, 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 | Step 3.7 Flash |
|---|---|---|---|
| Agentic | GLM-556.2 | Margin→ 10.2 | Step 3.7 Flash66.4 |
| Coding | GLM-566.3 | Margin← 10.0 | Step 3.7 Flash56.3 |
| Reasoning | GLM-560.8 | MarginNo overlap | Step 3.7 FlashNot measured |
| Knowledge | GLM-566.6 | MarginNo overlap | Step 3.7 FlashNot measured |
| Math | GLM-556.3 | MarginNo overlap | Step 3.7 FlashNot measured |
| Multilingual | GLM-583.1 | MarginNo overlap | Step 3.7 FlashNot measured |
| Inst. Following | GLM-592.6 | MarginNo overlap | Step 3.7 FlashNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 56.2%B 59.5%Winner: Step 3.7 FlashΔ 3.3Terminal-Bench 2.0: GLM-5 scored 56.2%; Step 3.7 Flash scored 59.5%. Step 3.7 Flash wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 55.1%B 56.3%Winner: Step 3.7 FlashΔ 1.2SWE-bench Pro: GLM-5 scored 55.1%; Step 3.7 Flash scored 56.3%. Step 3.7 Flash wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5 | Step 3.7 Flash | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | Step 3.7 Flash$0.2 input / $1.15 output | Step 3.7 Flash has the lower combined listed price. |
| Generation speedtokens per second | GLM-574 tok/s | Step 3.7 FlashNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-51.64 s | Step 3.7 FlashNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5200K | Step 3.7 Flash256K | Step 3.7 Flash lists the larger context window. |
Benchmark Deep Dive
AgenticStep 3.7 Flash wins19 benchmarks
| Benchmark | GLM-5 | Step 3.7 Flash | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 56.2% | 59.5% | Step 3.7 Flash leads |
| Claw-EvalSource | 57.7% | 67.1% | Step 3.7 Flash leads |
| QwenClawBenchSource | 54.1% | — | Not comparable |
| τ³-bench resultsSource | 65.6% | — | Not comparable |
| DeepPlanningSource | 14.6% | — | Not comparable |
| ToolathlonSource | 38% | 49.5% | Step 3.7 Flash leads |
| MCP AtlasSource | 31.1% | — | Not comparable |
| MCP-TasksSource | 60.8% | — | Not comparable |
| WideResearchSource | 69.8% | — | Not comparable |
| τ²-bench resultsSource | 98.2% | 98.5% | Step 3.7 Flash leads |
| CyberGymSource | 43.2% | — | Not comparable |
| APEX-Agents-AASource | 14.5% | 14.8% | Step 3.7 Flash leads |
| Gert LabsSource | 50.99% | 51.57% | Step 3.7 Flash leads |
| BrowseCompSource | — | 75.8% | Not comparable |
| DeepSearchQASource | — | 92.8% | Not comparable |
| GDPval-AASource | — | 25.9% | Not comparable |
| HLE w/ toolsSource | — | 47.2% | Not comparable |
| AA Agentic IndexSource | — | 21.5% | Not comparable |
| GDPval-AASource | — | 1017 | Not comparable |
CodingGLM-5 wins10 benchmarks
| Benchmark | GLM-5 | Step 3.7 Flash | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 77.8% | — | Not comparable |
| SWE-bench Verified*Source | 72.8% | — | Not comparable |
| SWE-bench ProSource | 55.1% | 56.3% | Step 3.7 Flash leads |
| SWE MultilingualSource | 73.3% | — | Not comparable |
| SWE-RebenchSource | 62.8% | — | Not comparable |
| React Native EvalsSource | 74.8% | — | Not comparable |
| Terminal-Bench HardSource | 43.2% | 35.6% | GLM-5 leads |
| AA-SciCodeSource | 46.2% | 40.0% | GLM-5 leads |
| Terminal-Bench 2.0Source | — | 59.5% | Not comparable |
| AA Coding IndexSource | — | 39.6% | Not comparable |
Reasoning4 benchmarks
Knowledge12 benchmarks
| Benchmark | GLM-5 | Step 3.7 Flash | 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% | 30.3% | GLM-5 leads |
| AA-GPQA DiamondSource | 82.0% | 80.9% | GLM-5 leads |
| AA-HLESource | 27.2% | 19.9% | GLM-5 leads |
| AA-Omniscience IndexSource | 2.0% | -37.5% | GLM-5 leads |
| AA-Omniscience AccuracySource | 26.9% | 25.4% | GLM-5 leads |
| AA-Omniscience Hallucination RateSource | 34.0% | 84.4% | GLM-5 leads |
Math8 benchmarks
| Benchmark | GLM-5 | Step 3.7 Flash | 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
Multimodal4 benchmarks
Frequently Asked Questions (3)
Which is better, GLM-5 or Step 3.7 Flash?
GLM-5 is ahead on BenchLM's provisional leaderboard, 63 to 57. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 56.2% and 59.5%.
Which is better for coding, GLM-5 or Step 3.7 Flash?
GLM-5 has the edge for coding in this comparison, averaging 66.3 versus 56.3. Inside this category, Terminal-Bench Hard is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GLM-5 or Step 3.7 Flash?
Step 3.7 Flash has the edge for agentic tasks in this comparison, averaging 66.4 versus 56.2. Inside this category, Toolathlon is the benchmark that creates the most daylight between them.
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