qwen-turbo-2024-11-01: benchmark scores, price and ranking

qwen-turbo-2024-11-01 from Alibaba ranks #633 of 743 LLMs on the LLMs Tiger aggregate benchmark score (44.5), based on 2 benchmark results captured up to 2026-10-13.

Organisation: Alibaba
Type: Frontier model
Open weights: No
Released: 2024-11-01 (source: name)
Cheapest API price: $0.050 in / $0.200 out per million tokens (via dashscope, observed 2026-10-13T20:47Z)
Aggregate rank: #633 of 743 (score 44.5)

Across the 2 benchmarks where at least ten models are scored, qwen-turbo-2024-11-01 is in the top 10% on 0 and in the top half on 0.

Its cheapest listed input price, $0.050 per million tokens, is lower than that of 90% of the 427 models with a listed price.

qwen-turbo-2024-11-01 benchmark results

qwen-turbo-2024-11-01 benchmark scores, ranked against all models measured on each benchmark
Benchmark	Score	Rank	Source	Captured
GPQA Diamond	41.8%	#217 of 275	Epoch AI Benchmarking Hub	2026-10-13
OTIS Mock AIME	6.1%	#154 of 189	Epoch AI Benchmarking Hub	2026-10-13

About qwen-turbo-2024-11-01

qwen-turbo-2024-11-01 is a frontier model from Alibaba. It was released on 2024-11-01, according to the model's own name. The cheapest API listing we track charges $0.050 per million input tokens and $0.200 per million output tokens, through dashscope. We hold 2 benchmark results for it across 1 area, and none is in the top tenth of its board.

Where qwen-turbo-2024-11-01 stands

On our aggregate score, qwen-turbo-2024-11-01 is #633 of 743 ranked models, in the lower half. Its score is 44.5; the scale is centred on 50, so it sits 5.5 points below the typical model. That beats 15% of the ranked field. Among the 74 ranked Alibaba models it is #69. The next one up is Qwen3.5 9B at 44.9. The next one down is qwen2-72b-instruct at 43.9.

qwen-turbo-2024-11-01, benchmark by benchmark

qwen-turbo-2024-11-01 lands below the middle on GPQA Diamond: 41.8%, #217 of 275, with 79% of the field scoring higher. GPT-6 Astra (max) leads at 95.8%; the closest result above is mistral-small-3.1-2503 at 41.9%. It covers expert-level science questions. The board median is 71.7%, so qwen-turbo-2024-11-01 is 29.9 points below it. Source: Epoch AI Benchmarking Hub, captured 2026-10-13.

OTIS Mock AIME is a weak spot. qwen-turbo-2024-11-01 scores 6.1%, #154 of 189, behind 81% of the models measured. The leader, Claude Fable 5.1 (max), reaches 100.0%. It covers competition maths (AIME 2024-25 style). The board median is 57.8%, so qwen-turbo-2024-11-01 is 51.7 points below it. Source: Epoch AI Benchmarking Hub, captured 2026-10-13.

Strengths and weaknesses

Reasoning, maths and science: weak, ahead of 20% of the field on average across 2 benchmarks. Its best board here is GPQA Diamond (#217 of 275) and its weakest is OTIS Mock AIME (#154 of 189).

Price and value

The median input price across the 427 priced models is $0.500 per million tokens, so qwen-turbo-2024-11-01 costs less than 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 amazon.nova-lite-v1:0 ($0.060) and qwen2.5-32b-instruct ($0.060).

Release history

The Alibaba model released before it was qwen2.5-7b-instruct on 2024-09-19, which scores 39.9. The next one was ollama/qwen2.5-coder:32b on 2024-11-12, at 51.9.

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

#630 Mistral-7B-Instruct-v0.2 (44.6)
#631 ollama/llama3.2:3b-instruct-fp16 (44.5)
#632 ollama/tulu3 (44.5)
#634 Mistral-7B-v0.2 (44.5)
#635 c4ai-command-a-03-2025 (44.5)
#636 DeepSeek-V3.2 (Thinking; Novita) (44.5)