MiniMax M2.7 vs GLM 5.2
At a Glance
| Compare |  MiniMax M2.7MiniMax |  GLM 5.2Z.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #23 of 4661.9 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 41.3–74.6 |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #12 of 44$0.056 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #7 of 3861.3 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 51.0–67.6 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.25Deepinfra ↗ · Sep 3, 2026 | $0.75Deepinfra ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | $1.00Deepinfra ↗ · Sep 3, 2026 | $2.40Deepinfra ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 205K | 1,049K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 28, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
| Benchmark | MiniMax-M2.7 | GLM-5.2 |
|---|---|---|
| LMArena Agent Arenaagent-2026-09-15-d25aabda0010 · outcome_score · leader | -14.0282% of row best · score · Minimax M2.7; 95% CI [-15.09137111, -12.95249504]; sessions 37764; observations 858163; rank 44 | 4.37100% of row best · score · GLM 5.2 (Max); 95% CI [3.67608698, 5.05982987]; sessions 76766; observations 4943866; rank 14 |
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,404.6196% of row best · rating · minimax-m2.7; 95% CI [1401.03588542, 1408.19095698]; votes 69169; rank 138 | 1,466.93100% of row best · rating · glm-5.2-max; 95% CI [1462.35303996, 1471.51268346]; votes 36798; rank 29 |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark winsNo overall winner | 2 benchmark winsNo overall winner |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Side-by-Side Facts
| Field | MiniMax-M2.7 | GLM-5.2 |
|---|---|---|
| Developer | MiniMax | Z.ai |
| Family | Minimax M2 7 | Glm 5 2 |
| Model | MiniMax-M2.7 | GLM-5.2 |
| Version | MiniMax-M2.7 | GLM-5.2 |
| Lifecycle | active | active |
| Released | 2026-03-18 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 205K | 1,049K |
| Total parameters | 228.7B | 753.3B |
| Active parameters | Unknown | Unknown |
| License | other | mit |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | chat, generation, tools | chat, generation, reasoning, tools |
MiniMax M2.7 Capabilities
GLM 5.2 Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
MiniMax-M2.7MiniMax | vs |  GPT-6 AstraOpenAI | cross-developer peerstext |
MiniMax-M2.7MiniMax | vs | Claude Fable 5.1Anthropic | cross-developer peerstext |
MiniMax-M2.7MiniMax | vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
MiniMax-M2.7MiniMax | vs |  DeepSeek-V4.1-FlashDeepSeek | cross-developer peerstext |
MiniMax-M2.7MiniMax | vs | Grok 4.6xAI | cross-developer peerstext |
MiniMax-M2.7MiniMax | vs |  Qwen3.8-MaxQwen | cross-developer peerstext |
MiniMax-M2.7MiniMax | vs |  Kimi-K3Moonshot AI | cross-developer peerstext |
MiniMax-M2.7MiniMax | vs | MiniMax-M3MiniMax | family variantstext |
MiniMax-M2.7MiniMax | vs | GLM-5.3Z.ai | cross-developer peerstext |
MiniMax-M2.7MiniMax | vs |  Hy4 previewTencent | cross-developer peerstext |
MiniMax-M2.7MiniMax | vs | Seed 2.1 ProByteDance Seed | cross-developer peerstext |
MiniMax-M2.7MiniMax | vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
MiniMax M2.7 vs GLM 5.2 FAQs
Is MiniMax M2.7 or GLM 5.2 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both MiniMax M2.7 and GLM 5.2, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, MiniMax M2.7 or GLM 5.2?+
MiniMax M2.7 is $0.25 and GLM 5.2 is $0.75 per million tokens, so MiniMax M2.7 is cheaper on this metric. MiniMax M2.7 is $1.00 and GLM 5.2 is $2.40 per million tokens, so MiniMax M2.7 is cheaper on this metric.
Which has a larger context window, MiniMax M2.7 or GLM 5.2?+
GLM 5.2 has the larger sourced context window. MiniMax M2.7 supports 205K and GLM 5.2 supports 1,049K.
Which performs better in benchmarks, MiniMax M2.7 or GLM 5.2?+
There is no overall benchmark winner: An overall winner requires at least two decisive benchmarks from at least two original publishers.
Can MiniMax M2.7 or GLM 5.2 be self-hosted?+
Both models have the same recorded self-hosting status: supported. MiniMax M2.7 is open weight; GLM 5.2 is open weight.
Can MiniMax M2.7 and GLM 5.2 understand images?+
MiniMax M2.7 is not documented with image input; GLM 5.2 is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, MiniMax M2.7 or GLM 5.2?+
Neither has a larger sourced maximum output. MiniMax M2.7 is — and GLM 5.2 is —.
Do MiniMax M2.7 and GLM 5.2 support reasoning and tool use?+
MiniMax M2.7: tool calling. GLM 5.2: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, MiniMax M2.7 or GLM 5.2?+
MiniMax M2.7 has 5 sourced provider routes; GLM 5.2 has 5, a tie.
Which offers better value, MiniMax M2.7 or GLM 5.2?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.