Ministral 3 3B Instruct 2512 vs GLM 5.3 Flash
At a Glance
| Compare |  Ministral 3 3B Instruct 2512Mistral AI |  GLM 5.3 FlashZ.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #29 of 4654.6 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 36.4–69.8 |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #2 of 44$0.0087 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #1 of 3877.3 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 68.2–84.9 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.10Openrouter ↗ · Aug 29, 2026 | $0.075Z.ai ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | $0.10Openrouter ↗ · Aug 29, 2026 | $0.25Z.ai ↗ · Aug 29, 2026 |
| Context windowMaximum documented tokens | 262K | 1,000K |
| Model facts checked | Aug 28, 2026View model evidence → | Sep 2, 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
Side-by-Side Facts
| Field | Ministral-3-3B-Instruct-2512 | GLM-5.3-Flash |
|---|---|---|
| Developer | Mistral AI | Z.ai |
| Family | Ministral 3 3b Instruct 2512 | Glm 5 3 Flash |
| Model | Ministral-3-3B-Instruct-2512 | GLM-5.3-Flash |
| Version | Ministral-3-3B-Instruct-2512 | GLM-5.3-Flash |
| Lifecycle | active | active |
| Released | Unknown | 2026-09-02 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image, Video, Document |
| Output modalities | Text | Text |
| Context window | 262K | 1,000K |
| Total parameters | 3.8B | 320B |
| Active parameters | Unknown | 18B |
| License | apache-2.0 | MIT |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Openrouter (Standard) | Deepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard), Z.ai (Standard) |
| Capabilities | chat, generation, tools | agents, chat, computer-use, reasoning, structured_outputs, tools, vision |
Ministral 3 3B Instruct 2512 Capabilities
GLM 5.3 Flash Capabilities
Related Comparisons
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|---|---|---|---|
Ministral-3-3B-Instruct-2512Mistral AI | vs |  GPT-6 AstraOpenAI | cross-developer peersimage, text |
Claude Fable 5.1Anthropic | vs | Ministral-3-3B-Instruct-2512Mistral AI | cross-developer peersimage, text |
 Gemini 3.1 ProGoogle DeepMind | vs | Ministral-3-3B-Instruct-2512Mistral AI | cross-developer peerstext |
 DeepSeek-V4.1-FlashDeepSeek | vs | Ministral-3-3B-Instruct-2512Mistral AI | cross-developer peersimage, text |
Ministral-3-3B-Instruct-2512Mistral AI | vs | Grok 4.6xAI | cross-developer peersimage, text |
Ministral-3-3B-Instruct-2512Mistral AI | vs |  Qwen3.8-MaxQwen | cross-developer peersimage, text |
Ministral-3-3B-Instruct-2512Mistral AI | vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text |
 MiniMax-M3MiniMax | vs | Ministral-3-3B-Instruct-2512Mistral AI | cross-developer peersimage, text |
Ministral-3-3B-Instruct-2512Mistral AI | vs | GLM-5.3Z.ai | cross-developer peerstext |
Ministral-3-3B-Instruct-2512Mistral AI | vs |  Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | Ministral-3-3B-Instruct-2512Mistral AI | cross-developer peersimage, text |
Ministral-3-3B-Instruct-2512Mistral AI | vs | Mistral Large 3Mistral AI | developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Ministral 3 3B Instruct 2512 vs GLM 5.3 Flash FAQs
Is Ministral 3 3B Instruct 2512 or GLM 5.3 Flash better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Ministral 3 3B Instruct 2512 and GLM 5.3 Flash, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Ministral 3 3B Instruct 2512 or GLM 5.3 Flash?+
Ministral 3 3B Instruct 2512 is $0.10 and GLM 5.3 Flash is $0.075 per million tokens, so GLM 5.3 Flash is cheaper on this metric. Ministral 3 3B Instruct 2512 is $0.10 and GLM 5.3 Flash is $0.25 per million tokens, so Ministral 3 3B Instruct 2512 is cheaper on this metric.
Which has a larger context window, Ministral 3 3B Instruct 2512 or GLM 5.3 Flash?+
GLM 5.3 Flash has the larger sourced context window. Ministral 3 3B Instruct 2512 supports 262K and GLM 5.3 Flash supports 1,000K.
Which performs better in benchmarks, Ministral 3 3B Instruct 2512 or GLM 5.3 Flash?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Ministral 3 3B Instruct 2512 or GLM 5.3 Flash be self-hosted?+
Both models have the same recorded self-hosting status: supported. Ministral 3 3B Instruct 2512 is open weight; GLM 5.3 Flash is open weight.
Can Ministral 3 3B Instruct 2512 and GLM 5.3 Flash understand images?+
Ministral 3 3B Instruct 2512 is documented with image input; GLM 5.3 Flash is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Ministral 3 3B Instruct 2512 or GLM 5.3 Flash?+
Neither has a larger sourced maximum output. Ministral 3 3B Instruct 2512 is — and GLM 5.3 Flash is 131K.
Do Ministral 3 3B Instruct 2512 and GLM 5.3 Flash support reasoning and tool use?+
Ministral 3 3B Instruct 2512: tool calling and image input. GLM 5.3 Flash: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Ministral 3 3B Instruct 2512 or GLM 5.3 Flash?+
Ministral 3 3B Instruct 2512 has 1 sourced provider route; GLM 5.3 Flash has 4, so GLM 5.3 Flash has broader tracked availability.
Which offers better value, Ministral 3 3B Instruct 2512 or GLM 5.3 Flash?+
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.