Model comparison
GLM-5 vs MiMo-V2-Flash
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 #28 (Supported); MiMo-V2-Flash #91 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and MiMo-V2-Flash share 14 comparable benchmark results. 2 of 8 categories are comparable. 35 results are unique to GLM-5; 5 to MiMo-V2-Flash.
Updated July 22, 2026- Shared results
- 14
- GLM-5 only
- 35
- MiMo-V2-Flash only
- 5
- Comparable categories
- 2 / 8
Pick GLM-5 if you want the stronger benchmark profile. MiMo-V2-Flash only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 14 shared benchmark results across 5 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 BenchAlign aggregate, 66.06 to 54.06. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5 is also the more expensive model on tokens at $1.00 input / $3.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for MiMo-V2-Flash. That is roughly Infinityx on output cost alone. MiMo-V2-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. MiMo-V2-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 | MiMo-V2-Flash |
|---|---|---|---|
| Knowledge | GLM-566.4 | Margin→ 18.3 | MiMo-V2-Flash84.7 |
| Coding | GLM-566.3 | Margin→ 7.1 | MiMo-V2-Flash73.4 |
| Agentic | GLM-556.2 | MarginNo overlap | MiMo-V2-FlashNot measured |
| Reasoning | GLM-560.8 | MarginNo overlap | MiMo-V2-FlashNot measured |
| Math | GLM-556.3 | MarginNo overlap | MiMo-V2-FlashNot measured |
| Multilingual | GLM-583.1 | MarginNo overlap | MiMo-V2-FlashNot measured |
| Inst. Following | GLM-592.6 | MarginNo overlap | MiMo-V2-FlashNot 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 73.4%Winner: GLM-5Δ 4.4SWE-bench Verified: GLM-5 scored 77.8%; MiMo-V2-Flash scored 73.4%. GLM-5 wins this benchmark. - Source ↗
GPQA
KnowledgeA 86%B 83.7%Winner: GLM-5Δ 2.3GPQA: GLM-5 scored 86%; MiMo-V2-Flash scored 83.7%. GLM-5 wins this benchmark. - Source ↗
MMLU-Pro
KnowledgeA 85.7%B 84.9%Winner: GLM-5Δ 0.8MMLU-Pro: GLM-5 scored 85.7%; MiMo-V2-Flash scored 84.9%. 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 | MiMo-V2-Flash | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | MiMo-V2-Flash$0 input / $0 output | MiMo-V2-Flash has the lower combined listed price. |
| Generation speedtokens per second | GLM-574 tok/s | MiMo-V2-Flash129 tok/s | MiMo-V2-Flash has the higher measured throughput. |
| First-answer latencyseconds to first token | GLM-51.64 s | MiMo-V2-Flash2.14 s | GLM-5 reaches the first token sooner. |
| Context windowmaximum listed tokens | GLM-5200K | MiMo-V2-Flash256K | MiMo-V2-Flash lists the larger context window. |
Benchmark Deep Dive
Agentic16 benchmarks
| Benchmark | GLM-5 | MiMo-V2-Flash | 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% | — | Not comparable |
| MCP-TasksSource | 60.8% | — | Not comparable |
| WideResearchSource | 69.8% | — | Not comparable |
| τ²-bench resultsSource | 98.2% | 83.9% | GLM-5 leads |
| CyberGymSource | 43.2% | — | Not comparable |
| APEX-Agents-AASource | 14.5% | — | Not comparable |
| Gert LabsSource | 50.99% | — | Not comparable |
| AA Agentic IndexSource | — | 12.0% | Not comparable |
| GDPval-AASource | — | 16.7% | Not comparable |
| GDPval-AASource | — | 833 | Not comparable |
CodingMiMo-V2-Flash wins8 benchmarks
| Benchmark | GLM-5 | MiMo-V2-Flash | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 77.8% | 73.4% | 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% | 25.9% | GLM-5 leads |
| AA Coding IndexSource | — | 49.8% | Not comparable |
Reasoning4 benchmarks
KnowledgeMiMo-V2-Flash wins12 benchmarks
| Benchmark | GLM-5 | MiMo-V2-Flash | Result |
|---|---|---|---|
| GPQASource | 86% | 83.7% | GLM-5 leads |
| GPQA-DSource | 86.0% | — | Not comparable |
| SuperGPQASource | 66.8% | — | Not comparable |
| MMLU-ProSource | 85.7% | 84.9% | GLM-5 leads |
| MMLU-Pro (Arcee)Source | 85.8% | — | Not comparable |
| HLESource | 50.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 39.5% | 24.7% | GLM-5 leads |
| AA-GPQA DiamondSource | 82.0% | 65.6% | GLM-5 leads |
| AA-HLESource | 27.2% | 8.0% | GLM-5 leads |
| AA-Omniscience IndexSource | 2.0% | -48.5% | GLM-5 leads |
| AA-Omniscience AccuracySource | 26.9% | 15.2% | GLM-5 leads |
| AA-Omniscience Hallucination RateSource | 34.0% | 75.1% | GLM-5 leads |
Math9 benchmarks
| Benchmark | GLM-5 | MiMo-V2-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 |
| AIME 2025Source | — | 94.1% | Not comparable |
Multilingual2 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-5 | MiMo-V2-Flash | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1278 | — | Not comparable |
Frequently Asked Questions (3)
Which is better, GLM-5 or MiMo-V2-Flash?
GLM-5 is ahead on BenchLM's BenchAlign leaderboard, 66.06 to 54.06. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 77.8% and 73.4%.
Which is better for knowledge tasks, GLM-5 or MiMo-V2-Flash?
MiMo-V2-Flash has the edge for knowledge tasks in this comparison, averaging 84.7 versus 66.4. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
Which is better for coding, GLM-5 or MiMo-V2-Flash?
MiMo-V2-Flash has the edge for coding in this comparison, averaging 73.4 versus 66.3. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
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