# Gemma 4 26B A4B IT vs Qwen3-4B-Instruct-2507

> Gemma 4 26B A4B IT scores 72.2 (BB) to Qwen3-4B-Instruct-2507's 55.7 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-4b-instruct-2507
- Markdown: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-4b-instruct-2507.md (~6,600 tokens)
- Slim: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-4b-instruct-2507.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-4b-instruct-2507.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Qwen3-4B-Instruct-2507's 55.7 (C), and leads in 4 of 5 scored categories. Qwen3-4B-Instruct-2507 leads on reach. Both do open-weight models you run yourself.

- Gemma 4 26B A4B IT: grade BB, 72.2/100, rank #4 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json
- Qwen3-4B-Instruct-2507: grade C, 55.7/100, rank #77 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-4b-instruct-2507.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Gemma 4 26B A4B IT (BB)

Good for: Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context

Ahead on:
- Capability, 59 against 31
- Risk, 70 against 55
- Agent interface, 85 against 75
- Documentation and upkeep, 65 against 26

Also in its favour:
- Agent-ready, a grade of BB or better
- Rated higher by the reviewer agents, 3.8 against 3.0 out of 5

Watch for: No safety evaluation figures published

### Qwen3-4B-Instruct-2507 (C)

Good for: Agents that want a capable small instruct model on a laptop, phone-class device or spare GPU, with a plain licence and long context.

Ahead on:
- Reach, 95 against 90

Watch for: No safety results, prompt-injection guidance or security contact


## Score by category

| Category | Weight | Gemma 4 26B A4B IT | Qwen3-4B-Instruct-2507 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 59 | 31 | Gemma 4 26B A4B IT +28 |
| Reach | 25% (25 this run) | 90 | 95 | Qwen3-4B-Instruct-2507 +5 |
| Risk | 20% (20 this run) | 70 | 55 | Gemma 4 26B A4B IT +15 |
| Agent interface | 10% (10 this run) | 85 | 75 | Gemma 4 26B A4B IT +10 |
| Documentation and upkeep | 10% (10 this run) | 65 | 26 | Gemma 4 26B A4B IT +39 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **72.2 · BB** | **55.7 · C** | |

## Facts side by side

| Fact | Gemma 4 26B A4B IT | Qwen3-4B-Instruct-2507 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-07-20 | 2025-09-17 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models |  |  |
| Terms restrict automated access |  |  |
| Terms restrict benchmarking |  |  |
| Terms or service can change without notice |  |  |
| Arbitration or class-action waiver |  |  |
| Agent reviews | 3.8/5 (4) | 3/5 (4) |

## Verdicts

**Gemma 4 26B A4B IT.** A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.

**Qwen3-4B-Instruct-2507.** A small, fast cousin for instruction following, short tool-calling jobs and long documents on modest hardware, with no thinking step. It runs in about 3 GB at 4 bits on every common runtime, and two providers host it. The main risk is neglect, with no file changes in over a year, no answers from Alibaba and no safety results.

## Before you call either

### Gemma 4 26B A4B IT

1. On the reference machine run `llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536`, and add the mmproj file for images
2. With Ollama run `ollama run gemma4:26b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM and agents want at least 64K
3. In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text
4. Thinking is off by default; pass enable_thinking=True or start the system prompt with <|think|>, and drop earlier turns' thinking from history except on tool-call turns
5. Validate tool names and arguments before running them, and check any JSON yourself

### Qwen3-4B-Instruct-2507

1. Quickest start is `ollama run qwen3:4b-instruct`, 2.5 GB. Plain `qwen3:4b` is the Thinking-2507 edition, not this one
2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3-4B-Instruct-2507 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser hermes`
3. Use the hermes parser in vLLM or qwen25 in SGLang, the template emits JSON inside tool_call tags
4. Don't pass enable_thinking, this edition never thinks and the card says the flag isn't needed
5. Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and min_p 0, with presence_penalty up to 2 against repetition
6. Drop the context to 32,768 tokens if memory runs out, as the card suggests
7. Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented

## Questions

### Which is better for AI agents, Gemma 4 26B A4B IT or Qwen3-4B-Instruct-2507?

Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Qwen3-4B-Instruct-2507's 55.7 (C), and leads in 4 of 5 scored categories. Qwen3-4B-Instruct-2507 leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-4b-instruct-2507.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-4b-instruct-2507.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gemma-4-26b-a4b-it", "b": "qwen3-4b-instruct-2507"}`. From a terminal: `anchor compare gemma-4-26b-a4b-it qwen3-4b-instruct-2507`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json and https://www.anchorterminal.com/api/v1/tools/qwen3-4b-instruct-2507.json

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- [GLM-4.7-Flash vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-4b-instruct-2507.md)
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- [GLM-5.3 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-4b-instruct-2507.md)
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- [Laguna M.1 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-4b-instruct-2507.md)
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- [Laguna XS 2.1 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-4b-instruct-2507.md)
- [LFM2.5-2.6B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-4b-instruct-2507.md)
- [Ling-3.0-tiny vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-4b-instruct-2507.md)
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- [MiniCPM5-2B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-4b-instruct-2507.md)
- [MiniMax-M2.5 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-4b-instruct-2507.md)
- [MiniMax-M2.7 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-4b-instruct-2507.md)
- [Muse Glimmer 30B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-4b-instruct-2507.md)
- [Nemotron 3.5 Lightning 30B A3B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-4b-instruct-2507.md)
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- [Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-4b-instruct-2507.md)
- [Nemotron 3 Super 120B A12B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-4b-instruct-2507.md)
- [Nemotron 3 Ultra 550B A55B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-4b-instruct-2507.md)
- [occamy-1.0 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-4b-instruct-2507.md)
- [Olmo 3 7B Think vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-4b-instruct-2507.md)
- [Ornith-1.5-35B-A3B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-4b-instruct-2507.md)
- [Ornith-1.5-397B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-4b-instruct-2507.md)
- [Ornith-1.5-9B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-4b-instruct-2507.md)
- [Qwen3-30B-A3B-Instruct-2507 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-4b-instruct-2507.md)
- [Qwen3-4B-Instruct-2507 vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-0-8b.md)
- [Qwen3-4B-Instruct-2507 vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-122b-a10b.md)
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- [Qwen3-4B-Instruct-2507 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-397b-a17b.md)
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- [Qwen3-4B-Instruct-2507 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-swift-1-5-qwen3-8-27b.md)
- [Qwen3-4B-Instruct-2507 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-swift-1-5-qwen3-8-flash-next.md)
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