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Model profile
eleutherai

EleutherAI: Llemma 7b

EleutherAI: Llemma 7b is a budget text-first model from eleutherai with a heavy runtime profile, compact context posture, and the clearest fit around reasoning / coding.

Best for: Reasoning / CodingHeavy latencyCompact contextBudget pricing
Intelligence
N/A

Benchmark blend

Coding
N/A

Dev workflow signal

Context
4K Tokens

Compact

Input Price
$0.80

Budget tier

Decision snapshot
48

EleutherAI: Llemma 7b currently reads as a budget text-first option with compact context and a heavy runtime profile.

Overall profile
Use-case specific
Best for
Reasoning / Coding
Latency tier
Heavy
Price tier
Budget
Source coverage
OpenRouter
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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
N/A
44

General reasoning and benchmark headroom.

Situational
Speed
N/A
46

Latency data is partial.

Situational
Context
4K Tokens
28

How much prompt and task state can stay in view.

Limited
Price
$0.80
86

$1.20 output / 1M

Efficient

Editorial Profile

EleutherAI: Llemma 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 40Math score 36

Llemma 7B is a language model for mathematics. It was initialized with Code Llama 7B weights, and trained on the Proof-Pile-2 for 200B tokens. Llemma models are particularly strong at chain-of-thought mathematical reasoning and using computational tools for mathematics, such as Python and formal theorem provers.

Identity

eleutherai text-first profile

Positioning

Reasoning / Coding with compact context and heavy runtime.

Cost posture

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

Strengths

  • The available source data suggests a balanced profile rather than one dominant edge.

Tradeoffs

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

  • Latency profile is better for deliberate runs than rapid back-and-forth chat.

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

  • Context window is more comfortable for focused tasks than extremely long sessions.

Best fit

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

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.

No benchmark data is available for this model yet.

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
4K Tokens
Vision
Text-first
Modalities
text->text, text
Tokenizer
Other
Max Completion
4096
Moderation
No
Supported Parameters
frequency_penaltymax_tokensmin_ppresence_penaltyrepetition_penaltyseedstoptemperaturetop_ktop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.80
Output
per 1M output tokens
$1.20
Blended
AA 3:1 mix
N/A

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.