Jais-3-375B-A23B

Jais-3-375B-A23B is the flagship of the Jais-3 family: a sparse Mixture-of-Experts model that stores 375B parameters and runs 23B per token, with a 512K context window.

[!TIP] An FP8-quantized version is available at IFM/Jais-3-375B-A23B-FP8.

Benchmark Results

[!NOTE] Starred (*) benchmarks have been audited for sample quality. Items with ambiguous questions, invalid answer options, or indefensible gold labels were excluded, since they can penalize a correct answer or reward a lucky guess. Scores on these benchmarks are reported on the cleaned sets. The audit methodology, the excluded samples and the verified benchmarks are available in the IFM/Arabic-Benchmark-Auditing collection.

[!NOTE] Benchmarks marked with ^ will be released by IFM soon.

Quickstart

Serving

The commands below reflect our reference deployment: 16× NVIDIA H100 (80 GB) across two 8-GPU nodes.

vLLM (vllm>=0.30.0) · vLLM recipe:

vllm serve inception42/Jais-3-375B-A23B \
  --trust-remote-code \
  --dtype bfloat16 \
  --tensor-parallel-size 16 \
  --enable-expert-parallel \
  --reasoning-parser k2_horizon \
  --enable-auto-tool-choice \
  --tool-call-parser k2_horizon

Use an exact branch name from the inventory with vLLM's --revision option.

SGLang (sglang>=0.5.20) · SGLang recipe:

# Multi-node (2 nodes). Run the same command on every node with:
#   <node-rank> = 0 on the head node, 1 on the other node
#   <node0-ip>  = IP of the head node (reachable from all others)
sglang serve \
  --model-path inception42/Jais-3-375B-A23B \
  --tp 16 \
  --nnodes 2 \
  --node-rank <node-rank> \
  --dist-init-addr <node0-ip>:20000 \
  --ep 16 \
  --dtype bfloat16 \
  --attention-backend fa3 \
  --model-loader-extra-config '{"enable_multithread_load":false}' \
  --reasoning-parser k2_horizon \
  --host 0.0.0.0 \
  --port 30000

[!NOTE] Running on H200. With 141 GB per GPU, the model fits on a single 8-GPU H200 node. In that case set tensor parallelism and expert parallelism to 8: --tensor-parallel-size 8 for vLLM, --tp 8 --ep 8 for SGLang.

API Usage

[!TIP] Recommended settings: reasoning_effort="high", the sampling hyperparameters in the table below, and at least 32,768 output tokens. Reasoning depth is selected per request through chat_template_kwargs. Thinking is returned in reasoning_content and the answer in content.

Reasoning ExampleTool Calling Example

from openai import OpenAI
client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
response = client.chat.completions.create(
    model="inception42/Jais-3-375B-A23B",
    messages=[{"role": "user", "content": "لدينا عددان مجموعهما 50 وحاصل ضربهما 600. ما هما العددان؟ اشرح خطوات الحل."}],
    temperature=0.7,
    top_p=0.95,
    max_tokens=32768,
    extra_body={"top_k": 20, "chat_template_kwargs": {"reasoning_effort": "high"}},
)
message = response.choices[0].message
print("Reasoning:", getattr(message, "reasoning_content", None))
print("Answer:", message.content)

import json
from datetime import datetime
from zoneinfo import ZoneInfo
from openai import OpenAI
client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
TIMEZONES = {"أبوظبي": "Asia/Dubai", "الرياض": "Asia/Riyadh", "القاهرة": "Africa/Cairo", "لندن": "Europe/London"}

def get_current_time(city: str) -> dict:
    if city not in TIMEZONES:
        return {"error": f"مدينة غير معروفة: {city}"}
    return {"city": city, "time": datetime.now(ZoneInfo(TIMEZONES[city])).strftime("%H:%M")}

tools = [{
    "type": "function",
    "function": {
        "name": "get_current_time",
        "description": "يعيد الوقت الحالي في مدينة معينة",
        "parameters": {
            "type": "object",
            "properties": {"city": {"type": "string", "description": "اسم المدينة بالعربية"}},
            "required": ["city"],
        },
    },
}]
available_tools = {"get_current_time": get_current_time}
messages = [{"role": "user", "content": "كم الساعة الآن في القاهرة؟"}]
for _ in range(5):  # call the model, run any requested tools, repeat until it answers
    message = client.chat.completions.create(
        model="inception42/Jais-3-375B-A23B",
        messages=messages,
        tools=tools,
        temperature=0.7,
        top_p=0.95,
        max_tokens=32768,
        extra_body={
            "top_k": 20,
            "chat_template_kwargs": {"reasoning_effort": "high", "tool_call_format": "xml"},
        },
    ).choices[0].message
    messages.append(message)
    if not message.tool_calls:
        break
    for call in message.tool_calls:
        tool = available_tools.get(call.function.name)
        arguments = json.loads(call.function.arguments)
        result = tool(**arguments)
        messages.append({"role": "tool", "tool_call_id": call.id,
                         "content": json.dumps(result, ensure_ascii=False)})

print(message.content)

Our model supports multiple tool-call formats, which can be changed with chat_template_kwargs. The supported values are json, xml, and xml_typed. The default is xml. Keep --tool-call-parser k2_horizon enabled to parse the selected format.

Transformers

Validated with Transformers 5.15.0, PyTorch 2.13.0, Safetensors 0.8.0.

from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "inception42/Jais-3-375B-A23B"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id, device_map="auto", dtype="bfloat16", low_cpu_mem_usage=True, trust_remote_code=True
)
inputs = tokenizer("لدينا عددان مجموعهما 50 وحاصل ضربهما 600. ما هما العددان؟ اشرح خطوات الحل.", return_tensors="pt").to(model.device)
inputs.pop("token_type_ids", None)
outputs = model.generate(**inputs, max_new_tokens=32768, temperature=0.7, top_p=0.95, top_k=20, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Release Artifacts

The table below lists the release artifacts for Jais-3-375B-A23B.

Artifact Index

Checkpoint Inventory

Model repository: inception42/Jais-3-375B-A23B

Branch names below refer to this repository.

Best Practices

1. Reasoning effort: always high. All reported results use high reasoning effort. Pass {"chat_template_kwargs": {"reasoning_effort": "high"}} on every request. No reasoning ({"reasoning_effort": "low"}) is not recommended except for research on reasoning efforts.
2. Sampling parameters. Use the values in the table above: temperature=0.2, top_p=0.95, top_k=20 by default. In thinking mode, set temperature=1.0 for English and temperature=0.7 for Arabic. Keep min_p=0.0, presence_penalty=0.0 and repetition_penalty=1.0.
3. Serving. Use the validated SGLang recipe above: BF16, TP=16 and EP=16 across two 8× H100 nodes, FlashAttention-3, with multithreaded weight loading disabled. On a single 8× H200 node, use TP=8 and EP=8 instead.
4. Parsers. Enable the k2_horizon reasoning parser for chat, and add the k2_horizon tool-call parser for agent use. Leave both off for plain completion-style generation.

Intended Use and Limitations

Intended use: The model is designed for Arabic and English assistant and agentic applications, including reasoning and tool-use tasks, as well as for research and experimentation.

Out of scope: The model should not be used in ways that violate applicable laws or regulations. The following uses are also considered out of scope:

• Harmful or malicious use: generating hateful, extremist, discriminatory, or deliberately misleading content, or using the model to support illegal or harmful activities.
• Sensitive information: using the model to generate, expose, or infer personal, confidential, or otherwise sensitive information about individuals or organizations.
• Unsupported languages: applications that depend on reliable performance in languages other than Arabic or English.
• High-stakes decisions: relying on the model to make medical, legal, financial, or safety-critical decisions without appropriate human oversight.

Limitations: The model can produce inaccurate or made-up content and may reflect biases present in its training data. Evaluate it for your use case and add safeguards before deploying it.

Citation

@misc{jais3_2026,
  title  = {Jais 3: Introducing Arabic-centric Agentic AI},
  author = {{IFM Team, Inception42 Team, and Cerebras Team}},
  year   = {2026},
  url    = {https://ifm.ai/jais/tech-report},
}