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Meta · Open weights

Llama-xLAM-2 70B fc-r

Added Apr 13, 2025Open weights

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Use in the app

Salesforce’s 70-B frontier model focused on function-calling & retrieval-augmented generation.

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$2.50
Output
$2.50

Reference prices as published by the model provider. API example

Specifications

Context window
128K
Max output
16.4K

Side by side

Llama-xLAM-2 70B fc-r compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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Llama-xLAM-2 70B fc-rMetaThis modelKimi K3 TEEMoonshotRemove
ProviderMetaMoonshot
Discovery tierOpen weightsOpen weights
AddedApr 13, 2025Jul 31, 2026
Limits
Context window128K1.05MBest
Max output16.38K65.54KBest
Average output—1.1K tokens
Pricing, USD per 1M tokens
Input$2.50Best$3.00
Output$2.50Best$15.00
Cache read—$1.50
Performance
Throughput—87.4 tok/s
Time to first token—3.2 s
Request success (24h)—98.0%
Benchmark indexes
Intelligence Index—43.6
Coding Index—76.2
Capabilities
ReasoningNoYes
ToolsNoYes
VisionNoYes
Structured outputNoYes
Reasoning effort—low, high, max

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • Llama-xLAM-2 70B fc-r
  • Kimi K3 TEE

Intelligence Index

  • Llama-xLAM-2 70B fc-r—
  • Kimi K3 TEE43.6
3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Llama-xLAM-2 70B fc-r: —
    • Kimi K3 TEE: 76.2

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Llama-xLAM-2 70B fc-r: —
    • Kimi K3 TEE: 58.3%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Llama-xLAM-2 70B fc-r: —
    • Kimi K3 TEE: 24.8%

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • Llama-xLAM-2 70B fc-r: —
    • Kimi K3 TEE: 5.3%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Llama-xLAM-2 70B fc-r: —
    • Kimi K3 TEE: 1,499 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Llama-xLAM-2 70B fc-r: —
    • Kimi K3 TEE: 1,534 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Llama-xLAM-2 70B fc-r: —
    • Kimi K3 TEE: 22.0%

  • AA-LCRv1.1

    Reasoning across long documents

    • Llama-xLAM-2 70B fc-r: —
    • Kimi K3 TEE: 88.7%

  • MLCR-AA

    Long-context reasoning over medical records

    • Llama-xLAM-2 70B fc-r: —
    • Kimi K3 TEE: 21.7%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Llama-xLAM-2 70B fc-r: —
    • Kimi K3 TEE: 46.9%

  • GPQA Diamond

    Graduate-level science questions

    • Llama-xLAM-2 70B fc-r: —
    • Kimi K3 TEE: 93.5%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Llama-xLAM-2 70B fc-r: —
    • Kimi K3 TEE: 12.6%

  • SciCode

    Scientific computing in Python

    • Llama-xLAM-2 70B fc-r: —
    • Kimi K3 TEE: 59.5%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Llama-xLAM-2 70B fc-r: —
    • Kimi K3 TEE: 88.7%

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Salesforce/Llama-xLAM-2-70b-fc-r",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="Salesforce/Llama-xLAM-2-70b-fc-r",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Meta

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