Model comparison
GLM-5 vs Kimi K2.7 Code
Head-to-head evidence from 13 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5 #28 (Supported); Kimi K2.7 Code #87 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and Kimi K2.7 Code share 13 comparable benchmark results. 0 of 8 categories are comparable. 36 results are unique to GLM-5; 10 to Kimi K2.7 Code.
Updated July 18, 2026- Shared results
- 13
- GLM-5 only
- 36
- Kimi K2.7 Code only
- 10
- Comparable categories
- 0 / 8
Benchmark data for GLM-5 and Kimi K2.7 Code is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 13 shared benchmark results across 6 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
Kimi K2.7 Code is priced at $0.95 input / $4.00 output per 1M tokens, versus $1.00 input / $3.20 output per 1M tokens for GLM-5. Kimi K2.7 Code has 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 | Kimi K2.7 Code |
|---|---|---|---|
| Agentic | GLM-556.2 | MarginNo overlap | Kimi K2.7 CodeNot measured |
| Coding | GLM-566.3 | MarginNo overlap | Kimi K2.7 CodeNot measured |
| Reasoning | GLM-560.8 | MarginNo overlap | Kimi K2.7 CodeNot measured |
| Knowledge | GLM-566.4 | MarginNo overlap | Kimi K2.7 CodeNot measured |
| Math | GLM-556.3 | MarginNo overlap | Kimi K2.7 CodeNot measured |
| Multilingual | GLM-583.1 | MarginNo overlap | Kimi K2.7 CodeNot measured |
| Inst. Following | GLM-592.6 | MarginNo overlap | Kimi K2.7 CodeNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5 | Kimi K2.7 Code | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | Kimi K2.7 Code$0.95 input / $4 output | GLM-5 has the lower combined listed price. |
| Generation speedtokens per second | GLM-574 tok/s | Kimi K2.7 CodeNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-51.64 s | Kimi K2.7 CodeNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5200K | Kimi K2.7 Code256K | Kimi K2.7 Code lists the larger context window. |
Benchmark Deep Dive
Agentic18 benchmarks
| Benchmark | GLM-5 | Kimi K2.7 Code | 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% | 76% | Kimi K2.7 Code leads |
| MCP-TasksSource | 60.8% | — | Not comparable |
| WideResearchSource | 69.8% | — | Not comparable |
| τ²-bench resultsSource | 98.2% | 90.1% | GLM-5 leads |
| CyberGymSource | 43.2% | — | Not comparable |
| APEX-Agents-AASource | 14.5% | — | Not comparable |
| Gert LabsSource | 50.99% | — | Not comparable |
| Kimi Claw 24/7Source | — | 46.9% | Not comparable |
| MCP Mark VerifiedSource | — | 81.1% | Not comparable |
| AA Agentic IndexSource | — | 29.6% | Not comparable |
| GDPval-AASource | — | 34.3% | Not comparable |
| GDPval-AASource | — | 1187 | Not comparable |
Coding12 benchmarks
| Benchmark | GLM-5 | Kimi K2.7 Code | 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% | 47.5% | Kimi K2.7 Code leads |
| Kimi Code Bench v2Source | — | 62.0% | Not comparable |
| ProgramBenchSource | — | 53.6% | Not comparable |
| MLS-Bench LiteSource | — | 35.1% | Not comparable |
| cursorBench32Source | — | 49.7% | Not comparable |
| AA Coding IndexSource | — | 60.8% | Not comparable |
Reasoning4 benchmarks
Knowledge12 benchmarks
| Benchmark | GLM-5 | Kimi K2.7 Code | 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% | 42.0% | Kimi K2.7 Code leads |
| AA-GPQA DiamondSource | 82.0% | 89.6% | Kimi K2.7 Code leads |
| AA-HLESource | 27.2% | 32.8% | Kimi K2.7 Code leads |
| AA-Omniscience IndexSource | 2.0% | -10.7% | GLM-5 leads |
| AA-Omniscience AccuracySource | 26.9% | 38.6% | Kimi K2.7 Code leads |
| AA-Omniscience Hallucination RateSource | 34.0% | 80.3% | GLM-5 leads |
Math8 benchmarks
| Benchmark | GLM-5 | Kimi K2.7 Code | 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 | Kimi K2.7 Code | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1280 | 1305 | Kimi K2.7 Code leads |
Frequently Asked Questions (3)
Can I compare GLM-5 and Kimi K2.7 Code on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for GLM-5 and Kimi K2.7 Code today?
GLM-5: $1.00 input / $3.20 output per 1M tokens Kimi K2.7 Code: $0.95 input / $4.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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