# GLM-4.7-Flash vs Spark-X2.6-4B

> GLM-4.7-Flash scores 63 (B) 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/glm-4-7-flash-vs-spark-x2-6-4b
- Markdown: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-spark-x2-6-4b.md (~6,100 tokens)
- Slim: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-spark-x2-6-4b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/glm-4-7-flash-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

GLM-4.7-Flash scores 63 (B) on agent readiness against Spark-X2.6-4B's 61 (C), and leads in 2 of 4 scored categories. Spark-X2.6-4B leads on reach and documentation and upkeep. Both do open-weight models you run yourself.

- GLM-4.7-Flash: grade B, 63/100, rank #26 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-4-7-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.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

### GLM-4.7-Flash (B)

Good for: Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime

Ahead on:
- Risk, 55 against 50
- Agent interface, 85 against 70

Watch for: No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January

### 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.

Ahead on:
- Reach, 85 against 80
- Documentation and upkeep, 41 against 28

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


## Score by category

| Category | Weight | GLM-4.7-Flash | Spark-X2.6-4B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 80 | 85 | Spark-X2.6-4B +5 |
| Risk | 20% (20 this run) | 55 | 50 | GLM-4.7-Flash +5 |
| Agent interface | 10% (10 this run) | 85 | 70 | GLM-4.7-Flash +15 |
| Documentation and upkeep | 10% (10 this run) | 28 | 41 | Spark-X2.6-4B +13 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **63 · B** | **61 · C** | |

## Facts side by side

| Fact | GLM-4.7-Flash | Spark-X2.6-4B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Z.ai (Zhipu AI) | SparkLLM (XHToken) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-01-29 | 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 | 2.8/5 (4) | 3/5 (4) |

## Verdicts

**GLM-4.7-Flash.** A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no safety results.

**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

### GLM-4.7-Flash

1. On our reference machine start it with `vllm serve zai-org/GLM-4.7-Flash --max-model-len 65536 --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash`
2. Or run `ollama run glm-4.7-flash` for the 19 GB q4_K_M build
3. Without the glm47 and glm45 parsers, tool calls and reasoning come back as plain text
4. Use a released vLLM (0.32.0 or later) or SGLang (0.5.22 or later); the card's nightly and pull-request builds date from January 2026
5. Pass `chat_template_kwargs: {"enable_thinking": false}` for short tasks, since thinking is on by default
6. Set `clear_thinking: false` to keep earlier reasoning in multi-turn agent work, as Z.ai does for τ²-Bench and Terminal Bench
7. Set the context length yourself, since config.json allows 202,752 tokens and tokenizer_config.json says 128,000

### 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, GLM-4.7-Flash or Spark-X2.6-4B?

GLM-4.7-Flash scores 63 (B) on agent readiness against Spark-X2.6-4B's 61 (C), and leads in 2 of 4 scored categories. Spark-X2.6-4B leads on reach and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-spark-x2-6-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-spark-x2-6-4b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "glm-4-7-flash", "b": "spark-x2-6-4b"}`. From a terminal: `anchor compare glm-4-7-flash spark-x2-6-4b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.json and https://www.anchorterminal.com/api/v1/tools/spark-x2-6-4b.json

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