# Jais 3 vs Muse Spark Open

> Muse Spark Open scores 53.4 (D) to Jais 3's 51.7 (D) for llm inference. Prices, MCP, x402, uptime and agent notes side by side.

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- Updated: 2026-10-17

Muse Spark Open scores 53.4 (D) on agent readiness against Jais 3's 51.7 (D), and leads in 4 of 7 scored categories. Jais 3 leads on reliability and payments & pricing. Both do llm inference.

- Jais 3: grade D, 51.7/100, rank #839 of 1133. Markdown https://www.anchorterminal.com/tools/jais-3.md · JSON https://www.anchorterminal.com/api/v1/tools/jais-3.json
- Muse Spark Open: grade D, 53.4/100, rank #789 of 1133. Markdown https://www.anchorterminal.com/tools/muse-spark-open.md · JSON https://www.anchorterminal.com/api/v1/tools/muse-spark-open.json
- Best model APIs and inference for AI agents: https://www.anchorterminal.com/best/inference/index.md
- All 326 models comparisons: https://www.anchorterminal.com/compare/inference/index.md

## Which one, for what

### Jais 3 (D)

Good for: An agent or team that needs Arabic and English reasoning and tool use on its own hardware, from a laptop-sized 0.9B to a cluster-sized 375B, under Apache-2.0.

Ahead on:
- Reliability, 48 against 42
- Payments & pricing, 60 against 50

Also in its favour:
- Free to start without a card

Watch for: Every documented vLLM, SGLang, Transformers and MLX command passes `--trust-remote-code`, so the repository's Python runs on the host

### Muse Spark Open (D)

Good for: A team with a four-GPU Blackwell node, or two to four Mac Studios, that wants a 1M-token multimodal coding and agent model on its own hardware with no prompts leaving it.

Ahead on:
- Schema & documentation, 61 against 54
- Security & auth, 44 against 31

Watch for: The NVFP4 checkpoint (about 537 GB) serves on four B300 GPUs and the BF16 one (about 1.65 TB) on eight


## Score by category

| Category | Weight | Jais 3 | Muse Spark Open | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 48 | 42 | Jais 3 +6 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 54 | 61 | Muse Spark Open +7 |
| Agent ergonomics | 13% (16.2 this run) | 65 | 69 | Muse Spark Open +4 |
| Security & auth | 14% (17.5 this run) | 31 | 44 | Muse Spark Open +13 |
| Payments & pricing | 10% (12.5 this run) | 60 | 50 | Jais 3 +10 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 62 | 60 | Jais 3 +2 |
| Transparency & trust | 7% (8.8 this run) | 50 | 53 | Muse Spark Open +3 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **51.7 · D** | **53.4 · D** | |

## Facts side by side

| Fact | Jais 3 | Muse Spark Open |
| --- | --- | --- |
| Kind | Model API | Model API |
| Vendor | Institute of Foundation Models (MBZUAI) and Inception42 | Meta |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | HTTP | HTTP |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0, declared in the metadata of every model card read. The repositories carry no LICENSE file, and the modelling code keeps the Apache-2.0 header of the Qwen3-MoE code it was generated from | Muse Model Licence (source-available weights, not an OSI licence), free of charge with an attribution notice, a separate agreement with Meta before Model as a Service by a licensee group above $20M gross revenue, and the Muse Spark Open Usage Policy |
| Read-only variant documented | no | no |
| llms.txt | no | yes |
| Last release | 2026-10-15 | 2026-10-15 |
| Terms last updated | 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) | none |

## Verdicts

**Jais 3.** Jais 3 runs on the owner's hardware through OpenAI-compatible servers in vLLM and SGLang, with tool calls, three reasoning levels and a 512K context, from a 2.2 GB 0.9B model to a 758 GB 375B one. The weights download without sign-up under Apache-2.0, but every launch command trusts remote code, and no technical report or security contact is published.

**Muse Spark Open.** Muse Spark Open runs on the owner's hardware in seven runtimes, with OpenAI-compatible servers in vLLM and SGLang, standard tool calls, a 1M-token context and Meta's own serving docs. Serving the 537 GB NVFP4 checkpoint takes four B300 GPUs, Meta's download is gated by manual review, and the licence adds attribution and a revenue threshold for model resellers.

## Before you call either

### Jais 3

1. Start vLLM or SGLang with `--reasoning-parser k2_horizon`, and add `--tool-call-parser k2_horizon` (vLLM also `--enable-auto-tool-choice`) for tools, or tool calls come back as raw text
2. Pass `reasoning_effort` (`high`, `medium` or `low`) and `tool_call_format` inside `chat_template_kwargs`. Other values return a template error
3. Send each earlier assistant turn back with `reasoning_content` as a string, even an empty one. A missing or null thinking field makes the template raise an error
4. Send your own system message, or set `default_system_prompt` to an empty string. Without one the template inserts an identity prompt naming IFM, Inception42 and Cerebras
5. Budget at least 32,768 output tokens at `high` effort, which the cards recommend for every request

### Muse Spark Open

1. Start vLLM or SGLang with `--reasoning-parser muse --tool-call-parser muse` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as raw text
2. Set `reasoning_effort` to `low` or `medium` for short tasks. The default is `xhigh`, reasoning can't be turned off, and other values return an error
3. Stop only on `<|eot|>` and end of text. Stopping on `<|eom|>` cuts off tool calls and final answers
4. Stream long generations and leave `max_tokens` room for reasoning, or `content` can come back empty with `finish_reason` `length`
5. Don't call Meta Model API for this model. Use a self-hosted server or a hosted provider's model string, such as `meta-models/muse-spark-open`

## Questions

### Which is better for AI agents, Jais 3 or Muse Spark Open?

Muse Spark Open scores 53.4 (D) on agent readiness against Jais 3's 51.7 (D), and leads in 4 of 7 scored categories. Jais 3 leads on reliability and payments & pricing.

### Do Jais 3 and Muse Spark Open need an API key?

Neither needs a key.

### Can an agent call Jais 3 and Muse Spark Open without installing anything?

No hosted endpoint is listed for Jais 3. No hosted endpoint is listed for Muse Spark Open.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/jais-3-vs-muse-spark-open.json, and with the fewest tokens: https://www.anchorterminal.com/compare/jais-3-vs-muse-spark-open.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "jais-3", "b": "muse-spark-open"}`. From a terminal: `anchor compare jais-3 muse-spark-open`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/jais-3.json and https://www.anchorterminal.com/api/v1/tools/muse-spark-open.json

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- [Claude API vs Muse Spark Open](https://www.anchorterminal.com/compare/anthropic-api-vs-muse-spark-open.md)
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