Head to head · LLM inference · October 2026 research run

Jais 3 vs 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. Both do llm inference.

Best model APIs and inference for AI agents · All 326 models comparisons

Which one, for what

Jais 3 D

Best 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

Best 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

CategoryWeight this runJais 3Muse Spark OpenEdge
Reliability16%204842Jais 3 +6
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.25461Muse Spark Open +7
Agent ergonomics13%16.26569Muse Spark Open +4
Security & auth14%17.53144Muse Spark Open +13
Payments & pricing10%12.56050Jais 3 +10
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.86260Jais 3 +2
Transparency & trust7%8.85053Muse Spark Open +3
Negative events≤1500
TotalD 51.7/100D 53.4/100

Facts side by side

FactJais 3Muse Spark Open
KindModel APIModel API
VendorInstitute of Foundation Models (MBZUAI) and Inception42Meta
Hosted endpointno (local only)no (local only)
TransportsHTTPHTTP
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-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 fromMuse 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 documentednono
llms.txtnoyes
Last release2026-10-152026-10-15
Terms last updatedno document linked
Privacy policy last updatedno document linkedno 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 reviews2.5/5 (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.

Other comparisons with Jais 3 or Muse Spark Open

Machine-readable

For companies

Do agents find, use and choose your tools?

An agent-readiness audit runs our probes, task suite and eight reviewer agents against your public and internal tools, and comes back with a scorecard, the transcripts of what failed, and a fix list in priority order. From $2,500, re-run included. We never take payment to move a rank. We do help companies earn one.