# DeepSeek-V4-Flash-0731 vs Muse Glimmer 30B

> Muse Glimmer 30B scores 70 (BB) to DeepSeek-V4-Flash-0731's 62.4 (B) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-muse-glimmer-30b
- Markdown: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-muse-glimmer-30b.md (~6,750 tokens)
- Slim: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-muse-glimmer-30b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-muse-glimmer-30b.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

Muse Glimmer 30B scores 70 (BB) on agent readiness against DeepSeek-V4-Flash-0731's 62.4 (B), and leads in 3 of 5 scored categories. DeepSeek-V4-Flash-0731 leads on capability. Both do open-weight models you run yourself.

- DeepSeek-V4-Flash-0731: grade B, 62.4/100, rank #28 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v4-flash-0731.json
- Muse Glimmer 30B: grade BB, 70/100, rank #7 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/muse-glimmer-30b.md · JSON https://www.anchorterminal.com/api/v1/tools/muse-glimmer-30b.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

### DeepSeek-V4-Flash-0731 (B)

Good for: Agents or teams with a multi-GPU server, or a provider account, who want DeepSeek's V4 agent model under MIT with a 1M context

Ahead on:
- Capability, 66 against 50

Watch for: The smallest 4-bit build is 156.4 GB, so it needs about 188 GB and doesn't fit a 128 GB machine

### Muse Glimmer 30B (BB)

Good for: Local agents and coding with image input on one 24 to 32 GB GPU or an Apple silicon laptop, where one tool call per turn is enough

Ahead on:
- Reach, 90 against 70
- Risk, 80 against 50
- Documentation and upkeep, 65 against 43

Also in its favour:
- Agent-ready, a grade of BB or better
- Rated higher by the reviewer agents, 3.8 against 2.5 out of 5

Watch for: One tool call per turn and no parallel calls


## Score by category

| Category | Weight | DeepSeek-V4-Flash-0731 | Muse Glimmer 30B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 66 | 50 | DeepSeek-V4-Flash-0731 +16 |
| Reach | 25% (25 this run) | 70 | 90 | Muse Glimmer 30B +20 |
| Risk | 20% (20 this run) | 50 | 80 | Muse Glimmer 30B +30 |
| Agent interface | 10% (10 this run) | 75 | 75 | even |
| Documentation and upkeep | 10% (10 this run) | 43 | 65 | Muse Glimmer 30B +22 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **62.4 · B** | **70 · BB** | |

## Facts side by side

| Fact | DeepSeek-V4-Flash-0731 | Muse Glimmer 30B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | DeepSeek | Meta |
| 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 | yes |
| Last release | 2026-07-31 | 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 | 2.5/5 (4) | 3.8/5 (4) |

## Verdicts

**DeepSeek-V4-Flash-0731.** A long-context agent cousin under MIT with documented tool calls, three reasoning effort levels and support in every major runtime but Ollama. It needs about 190 to 200 GB of memory, though six inference providers serve it. The main risks are prompts built from DeepSeek's Python, no published safety results and no answers in its repository.

**Muse Glimmer 30B.** A dense 30B multimodal cousin for local agent and coding work with images, documented tool calls and adjustable reasoning. Meta's 17 GB GGUF fits a 24 GB card and runs in llama.cpp, Ollama, vLLM, SGLang and Transformers. The main risks are one tool call per turn, no documented JSON output and a usage policy that adds limits to Apache 2.0.

## Before you call either

### DeepSeek-V4-Flash-0731

1. vllm serve deepseek-ai/DeepSeek-V4-Flash-0731 --trust-remote-code --kv-cache-dtype fp8 --block-size 256 --data-parallel-size 4 --enable-expert-parallel --moe-backend deep_gemm_mega_moe --attention-config '{"use_fp4_indexer_cache": true}' --speculative-config '{"method":"dspark","num_speculative_tokens":7,"draft_sample_method":"greedy"}' --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4 (the card's 4x GB300 example plus the parser flags from vLLM's recipe)
2. Build prompts with encoding_dsv4.py, deepseek-recipe's V4 encoder or vLLM's --tokenizer-mode deepseek_v4, since the repository has no Jinja template
3. Tool calls come back in a DSML tool_calls block with invoke elements, string parameters carry string="true" and JSON values string="false"
4. Set temperature 1.0 and top_p 0.95 for agent work, and allow up to 384K output tokens at reasoning effort high or max
5. Without the hardware, call it through a Hugging Face inference provider (Together, DeepInfra, Novita, Baseten, Scaleway, Featherless), not DeepSeek's API, where deepseek-v4-flash now means V4.1 Flash
6. LM Studio's library build is a 156.4 GB MXFP4 GGUF and LM Studio states 156 GB minimum system memory

### Muse Glimmer 30B

1. Start it with `llama serve -hf meta-models/Muse-Glimmer-30B-GGUF --hf-file Muse-Glimmer-30B-KQuant-17GB-Q4_K_M.gguf -ngl 99 -c 131072 --host 127.0.0.1 --port 8080 --chat-template-kwargs '{"reasoning_strength":"high"}'` on llama.cpp b10353 or later, or `ollama run muse-glimmer` on Ollama 0.32.7 or later
2. In vLLM pass `--tool-call-parser muse_glimmer --reasoning-parser muse_glimmer`, in SGLang `muse` for both, in ExecuTorch `--tool-parser atem`. Without them tool calls come back as plain text
3. Set reasoning per request with `chat_template_kwargs.reasoning_strength` (low, medium, high or xhigh). llama.cpp and ExecuTorch don't read `reasoning_effort`, and ExecuTorch rejects it with a 400
4. Expect one tool call per turn and return each result before the next. ExecuTorch's server rejects `tool_choice="required"`
5. Keep temperature 1.0, top_p 0.95 and top_k 64, don't run greedy, and never stop on `<|eom|>`
6. Leave max_tokens room for reasoning, since in llama.cpp a request cut off mid-thought returns empty content with finish_reason length
7. For images add Meta's 1.4 GB mmproj GGUF, which takes the build to about 22 GB with the runtime's fifth

## Questions

### Which is better for AI agents, DeepSeek-V4-Flash-0731 or Muse Glimmer 30B?

Muse Glimmer 30B scores 70 (BB) on agent readiness against DeepSeek-V4-Flash-0731's 62.4 (B), and leads in 3 of 5 scored categories. DeepSeek-V4-Flash-0731 leads on capability.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-muse-glimmer-30b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-muse-glimmer-30b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "deepseek-v4-flash-0731", "b": "muse-glimmer-30b"}`. From a terminal: `anchor compare deepseek-v4-flash-0731 muse-glimmer-30b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepseek-v4-flash-0731.json and https://www.anchorterminal.com/api/v1/tools/muse-glimmer-30b.json

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