# Gemma 4 31B IT vs Spark-X2.6-4B

> Gemma 4 31B IT scores 74.1 (BB) to Spark-X2.6-4B's 61 (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-4-31b-it-vs-spark-x2-6-4b
- Markdown: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-spark-x2-6-4b.md (~6,150 tokens)
- Slim: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-spark-x2-6-4b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-spark-x2-6-4b.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 31B IT scores 74.1 (BB) on agent readiness against Spark-X2.6-4B's 61 (C), and leads in every scored category. Both do open-weight models you run yourself.

- Gemma 4 31B IT: grade BB, 74.1/100, rank #2 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-31b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.json
- Spark-X2.6-4B: grade C, 61/100, rank #48 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/spark-x2-6-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/spark-x2-6-4b.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 31B IT (BB)

Good for: An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence

Ahead on:
- Reach, 95 against 85
- Risk, 70 against 50
- Agent interface, 85 against 70
- Documentation and upkeep, 65 against 41

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 results published

### Spark-X2.6-4B (C)

Good for: Agents that want a tiny local model for tool loops and long documents on a laptop, CPU or small GPU.

Watch for: No safety results and no agent or prompt-injection guidance


## Score by category

| Category | Weight | Gemma 4 31B IT | Spark-X2.6-4B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 95 | 85 | Gemma 4 31B IT +10 |
| Risk | 20% (20 this run) | 70 | 50 | Gemma 4 31B IT +20 |
| Agent interface | 10% (10 this run) | 85 | 70 | Gemma 4 31B IT +15 |
| Documentation and upkeep | 10% (10 this run) | 65 | 41 | Gemma 4 31B IT +24 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **74.1 · BB** | **61 · C** | |

## Facts side by side

| Fact | Gemma 4 31B IT | Spark-X2.6-4B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | SparkLLM (XHToken) |
| 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 | yes | no |
| Last release | 2026-07-20 | 2026-10-20 |
| 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 31B IT.** A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.

**Spark-X2.6-4B.** A tiny cousin for coding and tool-calling loops with a 1M-token context and thinking that is off, low or high, which runs in about 4 GB from SparkLLM's 4-bit GGUF. Use llama.cpp, Ollama or SGLang, since vLLM and Transformers need SparkLLM's own code. The risks are no safety work or agent guidance and a thin, two-day-old card.

## Before you call either

### Gemma 4 31B IT

1. On the reference machine run `llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768`, adding gemma-4-31B-it-mmproj.gguf for images
2. Or `ollama run gemma4:31b` for the 20 GB Q4_K_M build
3. In vLLM serve with `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4`, as vLLM's recipe does
4. Thinking is off by default, so pass `enable_thinking: true` in chat_template_kwargs to turn it on, and expect the reasoning in the thought channel block
5. Drop earlier thoughts from the history except on tool-call turns, as the card asks
6. Use the card's sampling, temperature 1.0, top_p 0.95, top_k 64
7. Validate function names and arguments before running a call, and check JSON output yourself or use the runtime's guided decoding
8. The w4a16 build relies on Marlin INT4 kernels that vLLM marks unsupported on AMD, so use the GGUF on the reference machine

### Spark-X2.6-4B

1. Quickest start is `llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q4_K_M --jinja`, which needs llama.cpp b10828 or later, or `ollama run SparkLLM/Spark-X2.6-4B` (8.2 GB, Ollama 0.34.1 or later)
2. For parsed tool calls serve it with SGLang 0.5.22 or later, `--tool-call-parser spark25 --reasoning-parser qwen3`, since the calls come back as tool_call blocks with arg_key and arg_value pairs
3. Thinking is on by default. Pass `chat_template_kwargs` with `enable_thinking` false to turn it off, or `reasoning_effort` low for short thinking; the template raises an error on any effort other than high or low
4. SparkLLM's recommended sampling is temperature 1.0, top_p 0.95 and top_k -1
5. The template always opens with its own default system line, with the effort instruction, and puts your system message after the tool list
6. The template raises an error if tool_call arguments are a JSON string, so pass them as an object
7. Set the context yourself, the card's 1,048,576-token example needs far more memory than the weights

## Questions

### Which is better for AI agents, Gemma 4 31B IT or Spark-X2.6-4B?

Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Spark-X2.6-4B's 61 (C), and leads in every scored category.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-spark-x2-6-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-spark-x2-6-4b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gemma-4-31b-it", "b": "spark-x2-6-4b"}`. From a terminal: `anchor compare gemma-4-31b-it spark-x2-6-4b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.json and https://www.anchorterminal.com/api/v1/tools/spark-x2-6-4b.json

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