Model comparison
GLM-5 vs Mistral Medium 3.5 128B
Head-to-head evidence from 14 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5 #31 (Supported); Mistral Medium 3.5 128B unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and Mistral Medium 3.5 128B share 14 comparable benchmark results. 1 of 8 categories are comparable. 35 results are unique to GLM-5; 11 to Mistral Medium 3.5 128B.
Updated July 27, 2026- Shared results
- 14
- GLM-5 only
- 35
- Mistral Medium 3.5 128B only
- 11
- 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; Mistral Medium 3.5 128B is the better fit if coding is the priority or you need the larger 256K context window.
Confidence note. This is a partial-evidence comparison with 14 shared benchmark results across 5 evidence categories; 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 Mistral Medium 3.5 128B 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.
Mistral Medium 3.5 128B is also the more expensive model on tokens at $1.50 input / $7.50 output per 1M tokens, versus $1.00 input / $3.20 output per 1M tokens for GLM-5. That is roughly 2.3x on output cost alone. Mistral Medium 3.5 128B 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. Mistral Medium 3.5 128B 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 | Mistral Medium 3.5 128B |
|---|---|---|---|
| Coding | GLM-566.3 | Margin→ 11.3 | Mistral Medium 3.5 128B77.6 |
| Agentic | GLM-556.2 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
| Reasoning | GLM-560.8 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
| Knowledge | GLM-566.4 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
| Math | GLM-556.3 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
| Multilingual | GLM-583.1 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
| Inst. Following | GLM-592.6 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Verified
CodingA 77.8%B 77.6%Winner: GLM-5Δ 0.2SWE-bench Verified: GLM-5 scored 77.8%; Mistral Medium 3.5 128B scored 77.6%. GLM-5 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5 | Mistral Medium 3.5 128B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | Mistral Medium 3.5 128B$1.5 input / $7.5 output | GLM-5 has the lower combined listed price. |
| Generation speedtokens per second | GLM-574 tok/s | Mistral Medium 3.5 128BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-51.64 s | Mistral Medium 3.5 128BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5200K | Mistral Medium 3.5 128B256K | Mistral Medium 3.5 128B lists the larger context window. |
Benchmark Deep Dive
Agentic21 benchmarks
| Benchmark | GLM-5 | Mistral Medium 3.5 128B | 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% | 91.4% | Mistral Medium 3.5 128B leads |
| 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% | 94.2% | GLM-5 leads |
| CyberGymSource | 43.2% | — | Not comparable |
| APEX-Agents-AASource | 14.5% | — | Not comparable |
| Gert LabsSource | 50.99% | 39.10% | GLM-5 leads |
| AA Agentic IndexSource | — | 19.0% | Not comparable |
| GDPval-AASource | — | 21.6% | Not comparable |
| GDPval-AASource | — | 933 | Not comparable |
| AA EnterpriseOps-GymSource | — | 33.7% | Not comparable |
| AA Harvey LABSource | — | 69.1% | Not comparable |
| terminalBenchHardSource | — | 33.3% | Not comparable |
| AA BriefcaseSource | — | 516 | Not comparable |
| AA Tau3 BankingSource | — | 14.4% | Not comparable |
CodingMistral Medium 3.5 128B wins8 benchmarks
| Benchmark | GLM-5 | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 77.8% | 77.6% | GLM-5 leads |
| 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% | 39.6% | GLM-5 leads |
| AA Coding IndexSource | — | 46.9% | Not comparable |
Reasoning4 benchmarks
Knowledge13 benchmarks
| Benchmark | GLM-5 | Mistral Medium 3.5 128B | 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% | 29.9% | GLM-5 leads |
| AA-GPQA DiamondSource | 82.0% | 74.8% | GLM-5 leads |
| AA-HLESource | 27.2% | 12.8% | GLM-5 leads |
| AA-Omniscience IndexSource | 2.0% | -36.3% | GLM-5 leads |
| AA-Omniscience AccuracySource | 26.9% | 25.1% | GLM-5 leads |
| AA-Omniscience Hallucination RateSource | 34.0% | 82.0% | GLM-5 leads |
| AA Openness IndexSource | — | 33.3% | Not comparable |
Math8 benchmarks
| Benchmark | GLM-5 | Mistral Medium 3.5 128B | 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
Multimodal2 benchmarks
Frequently Asked Questions (2)
Which is better, GLM-5 or Mistral Medium 3.5 128B?
GLM-5 and Mistral Medium 3.5 128B 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 coding, GLM-5 or Mistral Medium 3.5 128B?
Mistral Medium 3.5 128B has the edge for coding in this comparison, averaging 77.6 versus 66.3. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
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