OpenAI, proprietary

# GPT-3.5-turbo

> GPT-3.5-turbo by OpenAI, released March 2023. Ranked #350 of 354 with a Noometry Index of 23.2. API: $0.50 in / $1.50 out per M tokens. 16K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/gpt-3-5-turbo
- Last updated: 2026-10-10
- Title: GPT-3.5-turbo Benchmarks, Price & Rank (October 2026)

GPT-3.5-turbo by OpenAI ranks 350th of 354 ranked models on the Noometry Index as of October 2026, with a score of 23.2. Its strongest category is long context, where it ranks 254th. API pricing starts at $0.50 per million input tokens and $1.50 per million output tokens, with a 16K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #350 of 354
- **Index score:** 23.2
- **Evidence:** Confirmed 44 results
- **Provider:** [OpenAI](https://noometry.com/providers/openai)
- **Released:** March 1, 2023
- **Weights:** Proprietary
- **Reasoning:** No
- **Context window:** 16K
- **Max output:** 4K
- **Input price:** $0.50 / M
- **Output price:** $1.50 / M
- **Blended price:** $0.75 / M
- **Output speed:** Not measured
- **Value:** #133 of 219
- **Knowledge cutoff:** September 2021
- **Input:** text

## Category scores

Each category score combines every public result we have in that category.

GPT-3.5-turbo category scores

1.  Coding 23.9
2.  Reasoning 13.8
3.  Math 6.3
4.  Knowledge 10.0
5.  Multilingual 31.5
6.  Instruction Following 57.9
7.  Long Context 34.0
8.  Writing & Preference 25.3
9.  0204060

GPT-3.5-turbo category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 23.9 | #331 | 4 |
| [Reasoning](https://noometry.com/best/reasoning) | 13.8 | #332 | 5 |
| [Math](https://noometry.com/best/math) | 6.3 | #327 | 4 |
| [Knowledge](https://noometry.com/best/knowledge) | 10.0 | #303 | 2 |
| [Multilingual](https://noometry.com/best/multilingual) | 31.5 | #258 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 57.9 | #262 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 34.0 | #254 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 25.3 | #305 | 4 |

## Strengths and weaknesses

Categories where GPT-3.5-turbo places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

GPT-3.5-turbo: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Long Context](https://noometry.com/best/long-context) | 34.0 | −7.0 | #254 of 296, top 86% |
| [Instruction Following](https://noometry.com/best/instruction-following) | 57.9 | −13.4 | #262 of 305, top 86% |
| [Multilingual](https://noometry.com/best/multilingual) | 31.5 | −15.9 | #258 of 297, top 87% |

### Weakest categories

GPT-3.5-turbo: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Math](https://noometry.com/best/math) | 6.3 | −30.3 | #327 of 327, top 100% |
| [Writing & Preference](https://noometry.com/best/writing) | 25.3 | −28.5 | #305 of 312, top 98% |
| [Coding](https://noometry.com/best/coding) | 23.9 | −14.8 | #331 of 340, top 98% |

## Closest competitors

The models ranked just above and below GPT-3.5-turbo. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to GPT-3.5-turbo
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Claude 2](https://noometry.com/models/claude-2) | #346 | 25.0 | — | — | [Compare](https://noometry.com/compare/claude-2-vs-gpt-3-5-turbo) |
| [DeepSeek LLM 67B](https://noometry.com/models/deepseek-llm-67b) | #347 | 24.9 | — | — | [Compare](https://noometry.com/compare/deepseek-llm-67b-vs-gpt-3-5-turbo) |
| [Llama 13b](https://noometry.com/models/llama-13b) | #348 | 24.4 | — | — | [Compare](https://noometry.com/compare/gpt-3-5-turbo-vs-llama-13b) |
| [Llama 2-70B](https://noometry.com/models/llama-2-70b) | #349 | 24.4 | — | — | [Compare](https://noometry.com/compare/gpt-3-5-turbo-vs-llama-2-70b) |
| [Mistral 7B](https://noometry.com/models/mistral-7b) | #351 | 23.0 | $0.25 | — | [Compare](https://noometry.com/compare/gpt-3-5-turbo-vs-mistral-7b) |
| [Llama 3.1-8B](https://noometry.com/models/llama-3-1-8b) | #352 | 23.0 | $0.0575 | — | [Compare](https://noometry.com/compare/gpt-3-5-turbo-vs-llama-3-1-8b) |
| [Gemma 3 1B](https://noometry.com/models/gemma-3-1b) | #353 | 21.1 | — | — | [Compare](https://noometry.com/compare/gemma-3-1b-vs-gpt-3-5-turbo) |
| [Llama 3.2 1B](https://noometry.com/models/llama-3-2-1b) | #354 | 20.1 | $0.0705 | — | [Compare](https://noometry.com/compare/gpt-3-5-turbo-vs-llama-3-2-1b) |

Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise)

## Benchmark results

Every published result we track, with its source. Bold rows are the ones used for ranking; where several exist we prefer independent runs over self-reported numbers.

### Coding

GPT-3.5-turbo Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 3.5% | #117 of 119, top 99% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [BigCodeBench Instruct](https://noometry.com/benchmarks/bigcodebench-instruct) | 39.1% | #35 of 64, top 55% |  | [BigCodeBench](https://bigcode-bench.github.io/) | 2024-01-25 |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1136 | #260 of 294, top 89% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1116 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [BigCodeBench Complete](https://noometry.com/benchmarks/bigcodebench-complete) | 50.6% | #31 of 66, top 47% |  | [BigCodeBench](https://bigcode-bench.github.io/) | 2024-01-25 |
| [HumanEval+](https://noometry.com/benchmarks/humaneval-plus) | 66.5% |  | may 2023 | [EvalPlus](https://evalplus.github.io/leaderboard.html) |  |
| [HumanEval+](https://noometry.com/benchmarks/humaneval-plus) | 70.7% | #20 of 45, top 45% | nov 2023 | [EvalPlus](https://evalplus.github.io/leaderboard.html) |  |
| [MBPP+](https://noometry.com/benchmarks/mbpp-plus) | 69.7% | #14 of 38, top 37% | nov 2023 | [EvalPlus](https://evalplus.github.io/leaderboard.html) |  |

### Agentic & Tool Use

GPT-3.5-turbo Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [METR Time Horizons](https://noometry.com/benchmarks/metr-time-horizons) | 21.5% | #32 of 32, top 100% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

GPT-3.5-turbo Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 0% | #115 of 129, top 90% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1099 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1108 | #266 of 297, top 90% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 3% | #73 of 74, top 99% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 48.5% | #141 of 151, top 94% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 9.7% | #113 of 125, top 91% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Adversarial NLI](https://noometry.com/benchmarks/anli) | 58.1% | Best of 9 |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [BIG-Bench Hard](https://noometry.com/benchmarks/bbh) | 61.6% | #12 of 27, top 45% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CommonsenseQA 2.0](https://noometry.com/benchmarks/csqa2) | 57% | Best of 2 |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 115.7 |  |  | [Epoch AI](https://epoch.ai/eci) | 2024-01-25 |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 113.36 |  |  | [Epoch AI](https://epoch.ai/eci) | 2023-06-13 |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 118.55 | #174 of 213, top 82% |  | [Epoch AI](https://epoch.ai/eci) | 2023-11-06 |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 50.4 | #72 of 72, top 100% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WinoGrande](https://noometry.com/benchmarks/winogrande) | 81.6% | #11 of 43, top 26% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WinoGrande](https://noometry.com/benchmarks/winogrande) | 68.8% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

GPT-3.5-turbo Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 0% | #81 of 81, top 100% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 2.2% | #160 of 173, top 93% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-07 |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1141 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1142 | #256 of 285, top 90% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 11.6% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 15.9% | #66 of 79, top 84% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |
| [GSM8K](https://noometry.com/benchmarks/gsm8k) | 57.8% | #17 of 38, top 45% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Knowledge

GPT-3.5-turbo Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 28% | #173 of 186, top 94% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 27.2% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1070 | #256 of 273, top 94% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1066 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [ARC (AI2) Challenge](https://noometry.com/benchmarks/arc-challenge) | 87.4% | #7 of 39, top 18% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [BoolQ](https://noometry.com/benchmarks/boolq) | 87% | #5 of 23, top 22% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [MMLU](https://noometry.com/benchmarks/mmlu) | 71.4% | #41 of 81, top 51% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [MMLU](https://noometry.com/benchmarks/mmlu) | 68.9% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [MMLU](https://noometry.com/benchmarks/mmlu) | 67.3% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [OpenBookQA](https://noometry.com/benchmarks/openbookqa) | 86% | #4 of 19, top 22% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [TriviaQA](https://noometry.com/benchmarks/triviaqa) | 85.8% | #4 of 25, top 16% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Multilingual

GPT-3.5-turbo Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1074 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1108 | #258 of 297, top 87% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1075 | #260 of 285, top 92% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1012 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1066 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1118 | #210 of 223, top 95% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1090 | #212 of 231, top 92% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1056 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1043 | #191 of 211, top 91% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1019 | #197 of 213, top 93% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1077 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1123 | #252 of 283, top 90% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1093 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1121 | #211 of 226, top 94% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

GPT-3.5-turbo Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1119 | #259 of 298, top 87% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1093 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

GPT-3.5-turbo Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1070 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1121 | #262 of 291, top 91% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

GPT-3.5-turbo Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1125 | #266 of 297, top 90% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1094 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1092 | #266 of 295, top 91% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1035 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 451 | #114 of 115, top 100% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1117 | #259 of 295, top 88% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1078 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

GPT-3.5-turbo API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [azure](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models) | $0.50 | $1.50 | — | 2026-10-10 |
| [openai](https://platform.openai.com/docs/models) | $0.50 | $1.50 | Free | 2026-10-10 |
| [openrouter](https://openrouter.ai/openai/gpt-3.5-turbo) | $0.50 | $1.50 | — | 2026-10-10 |

[All OpenAI API prices →](https://noometry.com/llm-pricing/openai) [Estimate your cost →](https://noometry.com/tools/cost-calculator)

## Compare GPT-3.5-turbo

-   [GPT-3.5-turbo vs Llama 2-70B](https://noometry.com/compare/gpt-3-5-turbo-vs-llama-2-70b)
-   [GPT-3.5-turbo vs Mistral 7B](https://noometry.com/compare/gpt-3-5-turbo-vs-mistral-7b)
-   [GPT-3.5-turbo vs Llama 13b](https://noometry.com/compare/gpt-3-5-turbo-vs-llama-13b)
-   [GPT-3.5-turbo vs Llama 3.1-8B](https://noometry.com/compare/gpt-3-5-turbo-vs-llama-3-1-8b)
-   [GPT-3.5-turbo vs DeepSeek LLM 67B](https://noometry.com/compare/deepseek-llm-67b-vs-gpt-3-5-turbo)
-   [GPT-3.5-turbo vs Gemma 3 1B](https://noometry.com/compare/gemma-3-1b-vs-gpt-3-5-turbo)
-   [GPT-3.5-turbo vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-gpt-3-5-turbo)
-   [GPT-3.5-turbo vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-gpt-3-5-turbo)
-   [GPT-3.5-turbo vs Kimi K3](https://noometry.com/compare/gpt-3-5-turbo-vs-kimi-k3)
-   [GPT-3.5-turbo vs Grok 4.6](https://noometry.com/compare/gpt-3-5-turbo-vs-grok-4-6)
-   [GPT-3.5-turbo vs Qwen3.8 Max](https://noometry.com/compare/gpt-3-5-turbo-vs-qwen3-8-max)
-   [GPT-3.5-turbo vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-gpt-3-5-turbo)
-   [GPT-3.5-turbo vs Muse Spark 1.3](https://noometry.com/compare/gpt-3-5-turbo-vs-muse-spark-1-3)
-   [GPT-3.5-turbo vs DeepSeek V4 Pro](https://noometry.com/compare/deepseek-v4-pro-vs-gpt-3-5-turbo)

## Other OpenAI models

-   [GPT-6 Astra](https://noometry.com/models/gpt-6-astra)70.8
-   [GPT-6.1 Sol](https://noometry.com/models/gpt-6-1-sol)65.6
-   [GPT-5.6 Sol](https://noometry.com/models/gpt-5-6-sol)65.0
-   [GPT-5.5 Pro](https://noometry.com/models/gpt-5-5-pro)64.3
-   [GPT-5.5](https://noometry.com/models/gpt-5-5)63.4
-   [GPT-6 Sol](https://noometry.com/models/gpt-6-sol)61.8
-   [GPT-5.4](https://noometry.com/models/gpt-5-4)59.4
-   [GPT-5.6 Terra](https://noometry.com/models/gpt-5-6-terra)59.2

## Frequently asked questions

### How good is GPT-3.5-turbo?

GPT-3.5-turbo by OpenAI ranks 350th of 354 ranked models on the Noometry Index as of October 2026, with a score of 23.2. Its strongest category is long context, where it ranks 254th. API pricing starts at $0.50 per million input tokens and $1.50 per million output tokens, with a 16K-token context window.

### How much does GPT-3.5-turbo cost?

GPT-3.5-turbo costs $0.50 per million input tokens and $1.50 per million output tokens on OpenAI's own API.

### What is GPT-3.5-turbo's context window?

GPT-3.5-turbo accepts up to 16K tokens of input and can write up to 4K tokens in one response.

### Is GPT-3.5-turbo open source?

No. GPT-3.5-turbo is proprietary and available only through OpenAI's API and partner platforms.

### What are GPT-3.5-turbo's strengths and weaknesses?

Relative to other ranked models, GPT-3.5-turbo places best in long context, instruction following, multilingual and lowest in math, writing & preference, coding.

### What is GPT-3.5-turbo best at?

Its best category is long context, where it ranks 254th on Noometry.

### Cite this page

Noometry. (2026). GPT-3.5-turbo benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/gpt-3-5-turbo

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/gpt-3-5-turbo.md).