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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 modelMuse Glimmer 30BMetaRemove
ProviderMetaMeta
Discovery tierOpen weightsOpen weights
AddedApr 13, 2025Aug 11, 2026
Limits
Context window128K131.07KBest
Max output16.38K—
Average output—682 tokens
Pricing, USD per 1M tokens
Input$2.50$0.35Best
Output$2.50$1.50Best
Cache read—$0.04
Performance
Throughput—70.8 tok/s
Time to first token—1.1 s
Request success (24h)—100.0%
Benchmark indexes
Intelligence Index—17.5
Coding Index—49.0
Capabilities
ReasoningNoYes
ToolsNoYes
VisionNoYes
Structured outputNoYes
Reasoning effort—minimal, low, medium, high, xhigh

Public evaluations

Benchmarks

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

  • Llama-xLAM-2 70B fc-r
  • Muse Glimmer 30B

Intelligence Index

  • Llama-xLAM-2 70B fc-r—
  • Muse Glimmer 30B17.5
3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Llama-xLAM-2 70B fc-r: —
    • Muse Glimmer 30B: 49.0

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Llama-xLAM-2 70B fc-r: —
    • Muse Glimmer 30B: 6.8%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Llama-xLAM-2 70B fc-r: —
    • Muse Glimmer 30B: 0.8%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Llama-xLAM-2 70B fc-r: —
    • Muse Glimmer 30B: 477 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Llama-xLAM-2 70B fc-r: —
    • Muse Glimmer 30B: 783 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Llama-xLAM-2 70B fc-r: —
    • Muse Glimmer 30B: 10.0%

  • AA-LCRv1.1

    Reasoning across long documents

    • Llama-xLAM-2 70B fc-r: —
    • Muse Glimmer 30B: 83.3%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Llama-xLAM-2 70B fc-r: —
    • Muse Glimmer 30B: 22.0%

  • GPQA Diamond

    Graduate-level science questions

    • Llama-xLAM-2 70B fc-r: —
    • Muse Glimmer 30B: 83.5%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Llama-xLAM-2 70B fc-r: —
    • Muse Glimmer 30B: 0.5%

  • SciCode

    Scientific computing in Python

    • Llama-xLAM-2 70B fc-r: —
    • Muse Glimmer 30B: 44.9%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Llama-xLAM-2 70B fc-r: —
    • Muse Glimmer 30B: 83.3%

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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