MiniMax M3 vs GLM 5
At a Glance
| Compare |  MiniMax M3MiniMax |  GLM 5Z.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #35 of 4632.6 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 21.8–55.1 | UnrankedNot in the 46-model eligible cohort |
| CostLower is better · Published-token output estimate | #3 of 44$0.017 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| EfficiencyHigher is better · MM Efficiency v1.5 | #10 of 3859.2 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 53.8–70.4 | UnrankedNot in the 38-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.28Deepinfra ↗ · Sep 22, 2026 | $0.60Deepinfra ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $1.10Deepinfra ↗ · Sep 22, 2026 | $1.92Openrouter ↗ · Aug 28, 2026 |
| Context windowMaximum documented tokens | 1,049K | 203K |
| 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-M3 | GLM-5 |
|---|---|---|
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,433.5099% of row best · rating · minimax-m3; 95% CI [1429.31077607, 1437.68800840]; votes 48540; rank 82 | 1,446.27100% of row best · rating · glm-5; 95% CI [1441.95071870, 1450.59229773]; votes 27605; rank 51 |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 83.44100% of row best · points · MiniMax M3 · 16,142 output tokens / case | 82.8999% of row best · points · GLM-5 · 15,496 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark winsNo overall winner | 1 benchmark winNo 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-M3 | GLM-5 |
|---|---|---|
| Developer | MiniMax | Z.ai |
| Family | Minimax M3 | Glm 5 |
| Model | MiniMax-M3 | GLM-5 |
| Version | MiniMax-M3 | GLM-5 |
| Lifecycle | active | active |
| Released | 2026-06-01 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 1,049K | 203K |
| Total parameters | 427B | 753.9B |
| Active parameters | 23B | 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), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning, tools |
MiniMax M3 Capabilities
GLM 5 Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
MiniMax-M2.7MiniMax | vs | MiniMax-M3MiniMax | family variantstext |
MiniMax-M3MiniMax | vs |  GPT-6 AstraOpenAI | cross-developer peersimage, text |
MiniMax-M3MiniMax | vs | Claude Fable 5.1Anthropic | cross-developer peersimage, text |
MiniMax-M3MiniMax | vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
MiniMax-M3MiniMax | vs |  DeepSeek-V4.1-FlashDeepSeek | cross-developer peersimage, text |
MiniMax-M3MiniMax | vs | Grok 4.6xAI | cross-developer peersimage, text |
MiniMax-M3MiniMax | vs |  Qwen3.8-MaxQwen | cross-developer peersimage, text |
MiniMax-M3MiniMax | vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text |
MiniMax-M3MiniMax | vs | GLM-5.3Z.ai | cross-developer peerstext |
MiniMax-M3MiniMax | vs |  Hy4 previewTencent | cross-developer peerstext |
MiniMax-M3MiniMax | vs | Seed 2.1 ProByteDance Seed | cross-developer peersimage, text |
MiniMax-M3MiniMax | vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
MiniMax M3 vs GLM 5 FAQs
Is MiniMax M3 or GLM 5 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both MiniMax M3 and GLM 5, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, MiniMax M3 or GLM 5?+
MiniMax M3 is $0.28 and GLM 5 is $0.60 per million tokens, so MiniMax M3 is cheaper on this metric. MiniMax M3 is $1.10 and GLM 5 is $1.92 per million tokens, so MiniMax M3 is cheaper on this metric.
Which has a larger context window, MiniMax M3 or GLM 5?+
MiniMax M3 has the larger sourced context window. MiniMax M3 supports 1,049K and GLM 5 supports 203K.
Which performs better in benchmarks, MiniMax M3 or GLM 5?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can MiniMax M3 or GLM 5 be self-hosted?+
Both models have the same recorded self-hosting status: supported. MiniMax M3 is open weight; GLM 5 is open weight.
Can MiniMax M3 and GLM 5 understand images?+
MiniMax M3 is documented with image input; GLM 5 is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, MiniMax M3 or GLM 5?+
Neither has a larger sourced maximum output. MiniMax M3 is — and GLM 5 is —.
Do MiniMax M3 and GLM 5 support reasoning and tool use?+
MiniMax M3: reasoning, tool calling, and image input. GLM 5: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, MiniMax M3 or GLM 5?+
MiniMax M3 has 5 sourced provider routes; GLM 5 has 4, so MiniMax M3 has broader tracked availability.
Which offers better value, MiniMax M3 or GLM 5?+
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.