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Model profile
Arcee AI

Arcee AI: Coder Large

Arcee AI: Coder Large is a budget-priced text-first model from Arcee AI with partial runtime data, standard context posture, and the clearest fit around long-context research.

Best for: Long-context researchN/A latencyStandard contextBudget pricing
Coding models
Intelligence
N/A

Benchmark blend

Coding
N/A

Dev workflow signal

Context
33K Tokens

Standard

Input Price
$0.50

Budget tier

Decision snapshot
67

Arcee AI: Coder Large currently reads as a budget text-first option with standard context and a partially published runtime profile.

Overall profile
Selective fit
Best for
Long-context research
Latency tier
N/A
Price tier
Budget
Source coverage
OpenRouter
Open compare hubSee leaderboard

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
N/A

General reasoning and benchmark headroom.

Unavailable
Speed
N/A
N/A

Latency data is partial.

Unavailable
Context
33K Tokens
48

How much prompt and task state can stay in view.

Situational
Price
$0.50
86

$0.80 output / 1M

Efficient

Editorial Profile

Arcee AI: Coder Large in one narrative

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

Selective fitCoding score N/AMath score N/A

Coder‑Large is a 32 B‑parameter offspring of Qwen 2.5‑Instruct that has been further trained on permissively‑licensed GitHub, CodeSearchNet and synthetic bug‑fix corpora. It supports a 32k context window, enabling multi‑file...

Identity

Arcee AI text-first profile

Positioning

Long-context research with standard context and partially published 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 data is incomplete, so interactive responsiveness is harder to rank confidently.

  • 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.

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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.

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
33K Tokens
Vision
Text-first
Modalities
text
Tokenizer
Other
Moderation
No
Supported Parameters
frequency_penaltylogit_biasmax_tokensmin_ppresence_penaltyrepetition_penaltystoptemperaturetop_ktop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.50
Output
per 1M output tokens
$0.80
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.