Model profile
Meta

Llama 2 Chat 7B

Llama 2 Chat 7B is a budget-priced text-first model from Meta with fast runtime profile, partial context coverage, and the clearest fit around reasoning / coding.

Best for: Reasoning / CodingFast latencyN/A contextBudget pricing
Intelligence
9.7

Benchmark blend

Coding
0.002

Dev workflow signal

Context
N/A

N/A

Input Price
$0.05

Budget tier

Decision snapshot
40

Llama 2 Chat 7B currently reads as a budget text-first option with partially published context and a fast runtime profile.

Overall profile
Use-case specific
Best for
Reasoning / Coding
Latency tier
Fast
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
9.7
10

General reasoning and benchmark headroom.

Limited
Speed
123 tok/s
90

TTFT 0.60s

Above average
Context
N/A
N/A

How much prompt and task state can stay in view.

Unavailable
Price
$0.05
86

$0.25 output / 1M

Efficient

Editorial Profile

Llama 2 Chat 7B in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Use-case specificCoding score 0Math score 6

The Llama 2 Chat 7B AI model by Meta.

Identity

Meta text-first profile

Positioning

Reasoning / Coding with partially published context and fast runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths
  • Latency and throughput look responsive enough for interactive loops.

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Current metadata points to a text-first profile rather than a broad multimodal one.

  • Context limits are only partially published, so long-session planning needs extra validation.

Best fit
  • Focused chat, retrieval-augmented flows, and narrower production tasks.

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Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
9.7
MMLU Pro
16.4%
GPQA
22.7%
HLE
5.8%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.002
SciCode
0.0%
Math

Formal reasoning, structured problem solving, and competition-style math.

AIME
0.0%
Math 500
5.9%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
N/A
Vision
Text-first
Price architecture
Input
per 1M input tokens
$0.05
Output
per 1M output tokens
$0.25
Blended
AA 3:1 mix
$0.10

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.