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Model details

GPT-3.5 Turbo (older v0613)

GPT-3.5 Turbo (older v0613) is an OpenAI conversational language model in the broader GPT-3.5 family, designed to handle natural language and code generation with an emphasis on chat-style interactions and traditional completion tasks. A third-party spec listing describes the underlying model family as OpenAI's fastest release at the time, tuned for dialogue responsiveness while still supporting general-purpose text workflows. The model is positioned as a lighter, latency-friendly alternative within OpenAI's lineup, aimed at developers who need quick, interactive responses rather than the deeper reasoning capabilities of larger successors.

Practically, the snapshot fits workloads where short, fast text exchanges matter more than cutting-edge knowledge, such as customer support scripts, drafting assistance, code snippets, and lightweight automation. Its text-in, text-out design keeps integration straightforward for pipelines that only need language output, and its chat-optimized training makes it well suited to multi-turn conversational contexts. Because its training data extends only through late 2021, it is best deployed in scenarios that do not depend on recent world knowledge, leaving teams free to pair it with retrieval or external tools when up-to-date grounding is required.

OpenRouteropenai/gpt-3.5-turbo-0613gpt

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Provider
OpenRouter
Model key
openai/gpt-3.5-turbo-0613
Release date
Jan 25, 2024
Last updated
Jan 25, 2024
Knowledge cutoff
2021-09-30
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$1.00
Output token cost
$2.00

Limits

Output tokens
3,685 tokens
Context window
4,095 tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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Latest news about GPT-3.5 Turbo (older v0613)

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CoverageBenchmark

BenchmarkList maintains a dedicated evaluation profile for OpenAI's GPT-3.5 Turbo (older v0613), explicitly naming the exact snapshot variant served via OpenRouter. The page aggregates dated benchmark runs against the model, including a StableToolBench pass-rate average of 68.1 (40th percentile) recorded on 2026-05-06, Additional dated v0613-specific results on the same BenchmarkList page include a CRUXEval code-reasoning run on 2026-07-13 reporting an average pass@1 of 49.2% (65th percentile), with input pass@1 at 49.0%, output pass@1 at 49.4%, input pass@5 at 63.2%, and output pass@5 at 59.3%. An AlignBench general-knowledge score

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