GPT-3.5 Turbo (Nov 2023): benchmark scores, price and ranking
GPT-3.5 Turbo (Nov 2023) from OpenAI ranks #586 of 743 LLMs on the LLMs Tiger aggregate benchmark score (45.4), based on 2 benchmark results captured up to 2026-10-13.
- Organisation
- OpenAI
- Type
- Frontier model
- Open weights
- No
- Released
- 2023-11-06 (source: epoch)
- Cheapest API price
- $0.500 in / $1.50 out per million tokens (via azure, observed 2026-10-13T20:47Z)
- Aggregate rank
- #586 of 743 (score 45.4)
Across the 2 benchmarks where at least ten models are scored, GPT-3.5 Turbo (Nov 2023) is in the top 10% on 0 and in the top half on 0.
Its cheapest listed input price, $0.500 per million tokens, is lower than that of 48% of the 427 models with a listed price.
GPT-3.5 Turbo (Nov 2023) benchmark results
| Benchmark | Score | Rank | Source | Captured |
|---|---|---|---|---|
| Epoch ECI | 119 | #133 of 204 | Epoch AI Benchmarking Hub | 2026-10-13 |
| GPQA Diamond | 28.0% | #255 of 275 | Epoch AI Benchmarking Hub | 2026-10-13 |
About GPT-3.5 Turbo (Nov 2023)
GPT-3.5 Turbo (Nov 2023) is a frontier model from OpenAI. It was released on 2023-11-06, according to Epoch AI. The cheapest API listing we track charges $0.500 per million input tokens and $1.50 per million output tokens, through azure. We hold 2 benchmark results for it across 2 areas, and none is in the top tenth of its board.
Where GPT-3.5 Turbo (Nov 2023) stands
On our aggregate score, GPT-3.5 Turbo (Nov 2023) is #586 of 743 ranked models, in the lower half. Its score is 45.4; the scale is centred on 50, so it sits 4.6 points below the typical model. That beats 21% of the ranked field. Among the 88 ranked OpenAI models it is #72. The next one up is gpt-4-turbo at 45.4. The next one down is GPT-4.1 at 44.9.
GPT-3.5 Turbo (Nov 2023), benchmark by benchmark
GPT-3.5 Turbo (Nov 2023) lands below the middle on Epoch ECI: 119, #133 of 204, with 65% of the field scoring higher. Qwen 3.8 Max leads at 156; the closest result above is Mistral NeMo at 119. It covers Epoch Capabilities Index: Epoch's own aggregate of ~40 benchmarks. The board median is 127, so GPT-3.5 Turbo (Nov 2023) is 9 points below it. Source: Epoch AI Benchmarking Hub, captured 2026-10-13.
GPQA Diamond is a weak spot. GPT-3.5 Turbo (Nov 2023) scores 28.0%, #255 of 275, behind 92% of the models measured. The leader, GPT-6 Astra (max), reaches 95.8%. It covers expert-level science questions. The board median is 71.7%, so GPT-3.5 Turbo (Nov 2023) is 43.7 points below it. Source: Epoch AI Benchmarking Hub, captured 2026-10-13.
Strengths and weaknesses
GPT-3.5 Turbo (Nov 2023) has too few results on boards with ten or more models to say where it is strong or weak. Treat any single score as a data point, not a verdict.
Price and value
The median input price across the 427 priced models is $0.500 per million tokens, so GPT-3.5 Turbo (Nov 2023) costs about the same as the typical listing. Models that score within 1.5 points or better and cost less: Qwen2.5-Coder-7B-Instruct ($0.010, 49.5), DeepSeek V4.1 Flash (high) ($0.010, 48.9), Qwen2.5-Coder-3B-Instruct ($0.010, 46.5). Similar scores cost more on Llama-4-Maverick-17B-128E-Instruct ($0.630) and Llama-2-70b-chat ($0.650).
Release history
The OpenAI model released before it was GPT-4 (Jun 2023) on 2023-06-13, which scores 41.2. The next one was GPT-4 (1106) on 2023-11-06, at 46.9.
Reading these numbers
The 2 scores on this page come from 1 source: Epoch AI Benchmarking Hub (2). No official leaderboard supplied any of them, so treat them as third-party measurements. With 2 results the aggregate rank can move a lot when one new score arrives.
Models ranked nearby
- #583 Claude 3.5 Sonnet (Oct 2024) (45.4)
- #584 phi-4 (45.4)
- #585 gpt-4-turbo (45.4)
- #587 internlm2-math-plus-20b (45.4)
- #588 LLaDA-8B-Instruct (45.4)
- #589 Llama-3-Smaug-8B (45.4)
More from OpenAI
- GPT-6 Astra (max) (#2, 64.0)
- GPT-6.1 Sol (max) (#5, 60.6)
- GPT-5.6 Sol (max) (#6, 60.1)
- GPT-6 Sol (max) (#7, 60.0)
- GPT-5.5 (xhigh) (#11, 58.0)
- GPT-5.6 Terra (max) (#12, 57.8)
Compare it on the live leaderboard → · How the aggregate score works