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Model comparison
Google's Gemma 3 4B against Mistral AI's Ministral 3 3B 2512: vendor list prices, limits, abilities and the Artificial Analysis indexes side by side - and what OpenSmartRoute does when both are available.
Gemma 3 4B lists at $0.063 per million tokens blended against $0.1 for Ministral 3 3B 2512 - 1.6x less.
Intelligence index 5 against 5: within a point of each other.
Ministral 3 3B 2512 scores 5 on the coding index against 3 - 2 points apart.
only Ministral 3 3B 2512 offers tool calls.
Ministral 3 3B 2512 was released about 9 months later.
Vendor list prices per million tokens and published limits; indexes are Artificial Analysis composites (0-100).
| Specification | Gemma 3 4B | Ministral 3 3B 2512 |
|---|---|---|
| Input price / 1M tokens | $0.05 | $0.10 |
| Output price / 1M tokens | $0.10 | $0.10 |
| Blended (3 in : 1 out) | $0.06 | $0.10 |
| Cached input / 1M | - | $0.01 |
| Context window | 131K | 131K |
| Max output | 16K | 105K |
| Input | text, image | text, image |
| Output | text | text |
| Tool calls | ||
| Structured output | ||
| Reasoning | ||
| Open weights | ||
| Intelligence index | 5 | 5 |
| Coding index | 3 | 5 |
| Agentic index | - | 1 |
| Released | Mar 13, 2025 | Dec 2, 2025 |
| Available on this deployment |
List price times tokens for three typical workloads; your mix of input and output decides which model's pricing suits you.
| Workload | Gemma 3 4B | Ministral 3 3B 2512 | Difference |
|---|---|---|---|
| Support chat20M tokens in, 5M out a month - a help desk answering a few thousand conversations. | $1.50 | $2.50 | $1.00 less on Gemma 3 4B |
| Document summaries100M in, 5M out - long inputs, short answers. | $5.50 | $10.50 | $5.00 less on Gemma 3 4B |
| Coding assistant60M in, 20M out - a team's editor assistant, output-heavy. | $5.00 | $8.00 | $3.00 less on Gemma 3 4B |
The two measure the same on the intelligence index, so the router treats them as peers and lets price, observed latency and your outcomes decide: Gemma 3 4B wins on cost, and a few bad outcomes on either move the next requests to the other.
# targets.yaml - both models as routing targets (metadata.model links each to the catalogue entry)
targets:
- { id: gemma-3-4b-it, kind: llm, metadata: { model: google/gemma-3-4b-it } }
- { id: ministral-3b-2512, kind: llm, metadata: { model: mistralai/ministral-3b-2512 } }
# rules.yaml - the cheaper model by default, the stronger one when the request is hard
rules:
- name: simple-requests-stay-cheap
when: { max_complexity: 0.45 }
prefer: [gemma-3-4b-it]
weight: 0.8
- name: hard-requests-escalate
when: { min_complexity: 0.45 }
prefer: [ministral-3b-2512]
weight: 0.8Every Google and Mistral AI model, with prices, is on the vendor pages; the rankings show what the router actually picks.