Head to head · LLM inference · October 2026 research run

Jais 3 vs Nemotron 3.5 Super VL

Nemotron 3.5 Super VL scores 58.9 (C) on agent readiness against Jais 3's 51.7 (D), and leads in 5 of 7 scored categories. Jais 3 leads on 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

  • Payments & pricing, 60 against 35

Also in its favour

  • No key needed to call it

Watch for

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

Nemotron 3.5 Super VL C

Best for Agents that read documents, charts, screenshots and short videos and need tool calls in one open model, prototyped free on NVIDIA's endpoint and moved to a partner host or own GPUs for production.

Ahead on

  • Reliability, 58 against 48
  • Schema & documentation, 74 against 54
  • Security & auth, 51 against 31
  • Maintenance & community, 71 against 62
  • Transparency & trust, 59 against 50

Also in its favour

  • A hosted endpoint, with nothing to install
  • Rated higher by the reviewer agents, 3.0 against 2.5 out of 5

Watch for

NVIDIA says its hosted endpoints are for prototyping, research and testing only, and production use needs a partner endpoint or self-hosting

Score by category

CategoryWeight this runJais 3Nemotron 3.5 Super VLEdge
Reliability16%204858Nemotron 3.5 Super VL +10
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.25474Nemotron 3.5 Super VL +20
Agent ergonomics13%16.26565even
Security & auth14%17.53151Nemotron 3.5 Super VL +20
Payments & pricing10%12.56035Jais 3 +25
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.86271Nemotron 3.5 Super VL +9
Transparency & trust7%8.85059Nemotron 3.5 Super VL +9
Negative events≤1500
TotalD 51.7/100C 58.9/100

Facts side by side

FactJais 3Nemotron 3.5 Super VL
KindModel APIModel API
VendorInstitute of Foundation Models (MBZUAI) and Inception42NVIDIA
Hosted endpointno (local only)https://integrate.api.nvidia.com/v1
TransportsHTTPHTTP
AuthNoneAPI key
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 fromOpenMDW-1.1 for the weights (permissive, not an OSI licence). The hosted trial endpoint is under the NVIDIA API Trial Terms of Service, and the NIM container under the NVIDIA Software Licence Agreement
Read-only variant documentednono
llms.txtnoyes
Last release2026-10-152026-10-15
Terms last updatedno document linked
Privacy policy last updatedno document linkedno date given
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)3/5 (4)

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.

Nemotron 3.5 Super VL

NVIDIA's free endpoint takes OpenAI-format requests with typed tool calls, JSON Schema output and an OpenAPI 3.1 file, and the weights run anywhere under a permissive licence. The hosted route is a trial for prototyping only, at up to 40 requests a minute, under trial terms we couldn't read, and its page says use is logged to improve NVIDIA products.

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

Nemotron 3.5 Super VL

  1. Set chat_template_kwargs to {"enable_thinking": false} for short tasks. Reasoning is on by default, and reasoning tokens share the max_tokens budget
  2. Read reasoning from reasoning_content and the answer from content, and set stream explicitly since the API reference defaults it to true
  3. Don't send confidential or personal data to the hosted trial. The model page asks callers not to and says use is logged
  4. Back off and retry on throttling. Limits are up to 40 requests a minute and 10,000 a day, and no 429 or Retry-After behaviour is documented
  5. For production, use a partner endpoint or serve the weights. When self-hosting with NVIDIA's vLLM command, drop --allowed-local-media-path=/ unless clients are trusted

Questions

Which is better for AI agents, Jais 3 or Nemotron 3.5 Super VL?

Nemotron 3.5 Super VL scores 58.9 (C) on agent readiness against Jais 3's 51.7 (D), and leads in 5 of 7 scored categories. Jais 3 leads on payments & pricing.

Do Jais 3 and Nemotron 3.5 Super VL need an API key?

Jais 3 needs no key. Nemotron 3.5 Super VL needs an API key.

Can an agent call Jais 3 and Nemotron 3.5 Super VL without installing anything?

No hosted endpoint is listed for Jais 3. Nemotron 3.5 Super VL has a hosted endpoint at https://integrate.api.nvidia.com/v1.

Other comparisons with Jais 3 or Nemotron 3.5 Super VL

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