AlibabaQwen2.5 7B Instruct
Qwen2.5 7B Instruct is an Alibaba open-weights model, released 19 Sept 2024. Its best configuration ranks #670 of 785 on the BenchLeader Index at 39.9 ±6.0, in the lower half. The ± is the point: 289 other configurations score within that range, so they and this one cannot be told apart on quality alone — price and speed are what separate them. It scores highest in instruction following (37) and lowest in reasoning (28). At $0.125 per million tokens blended it is among the cheapest fifth of ranked models. Output speed of 79 tokens per second puts it slower than most, with a first token in 0.3 s. Last measured 24 Nov 2025.stale: no new result in six months
- Blended price
- $0.125/M
- $0.100 in · $0.200 out
- Output speed
- 79 tok/s
- OpenRouter traffic, 7-day median; not yet measured by Artificial Analysis
- First answer
- 0.27 s
- Context
- 33k
- Full answer
- –
- Cost per run
- –
- Released
- 19 Sept 2024
- Overall index40
- Reasoning28
- Maths37
- Instruction following37
How its index has moved
12 Sept 2026 to 21 Oct 2026The index is recomputed from scratch every day, so a line moves when a new benchmark result lands, when a publisher revises a score, or when the models it is normalised against change. Early movement usually means the score is still settling.
Benchmark results
See all LLM benchmarks. One column per reasoning effort. Rank is among every configuration of every model on that benchmark. Hover a score for the run it came from.
Reasoning
| Benchmark | not stated | Source | Trend |
|---|---|---|---|
| GPQA Diamond | 35.5%#259 | Epoch AI Benchmarking Hub | |
| GPQA Diamond (HELM)not in index | 34.1%#56 | HELM Capabilities | |
| LMCAnot in index | 6.4%#186 | Epoch AI Benchmarking Hub | |
| DTBenchnot in index | 47.7%#221 | Epoch AI Benchmarking Hub |
Maths
| Benchmark | not stated | Source | Trend |
|---|---|---|---|
| OTIS Mock AIME | 2.5%#255 | Epoch AI Benchmarking Hub | |
| Omni-MATH (HELM) | 29.4%#45 | HELM Capabilities |
Knowledge
| Benchmark | not stated | Source | Trend |
|---|---|---|---|
| MMLU-Pro (HELM)not in index | 53.9%#54 | HELM Capabilities | |
| MMLUnot in index | 72.9%#46 | Epoch AI Benchmarking Hub |
Instruction following
| Benchmark | not stated | Source | Trend |
|---|---|---|---|
| IFEval (HELM) | 74.1%#59 | HELM Capabilities | |
| WildBench (HELM) | 73.1%#57 | HELM Capabilities |
Composite
| Benchmark | not stated | Source | Trend |
|---|---|---|---|
| Epoch Capabilities Indexnot in index | 118.5#181 | Epoch AI Benchmarking Hub | |
| HELM Capabilities meannot in index | 52.9%#55 | HELM Capabilities |
Where to run it
Every provider serving this model through OpenRouter, with throughput and first-token latency measured on live traffic over the last 30 minutes and each provider’s own price. Purple marks the best in each column.
| Provider | Speed | First token | Input $/M | Output $/M | Context | Quantisation |
|---|---|---|---|---|---|---|
| Phala | 77 tok/s | 0.24 s | $0.100 | $0.200 | 33k | – |
Price history
The price above, per 1M tokens, every day it changed.
What a task costs
Estimates from list price, output speed and time to first answer for the best configuration. “With caching” assumes three-quarters of the input is served from the prompt cache. Reasoning tokens are not modelled.
| Workload | Tokens in / out | Cost | With caching | Time |
|---|---|---|---|---|
| Chat reply | 400 / 300 | $0.0001 | – | 4.1 s |
| Summarize a 30-page report | 12,000 / 600 | $0.0013 | – | 7.9 s |
| Code edit | 6,000 / 1,500 | $0.0009 | – | 19.3 s |
| Agentic coding session | 60,000 / 4,000 | $0.0068 | – | 50.9 s |
| Structured extraction | 2,000 / 200 | $0.0002 | – | 2.8 s |
See also
Questions people ask
- What are Qwen2.5 7B Instruct's benchmark scores?
- As of 25 Oct 2026, Qwen2.5 7B Instruct scores 39.9 ±6.0 on the BenchLeader Index, strongest in instruction following (37) and weakest in reasoning (28). That rests on 5 independent benchmark results, and every score on this page links to its publisher.
- How does Qwen2.5 7B Instruct rank?
- Qwen2.5 7B Instruct is #670 of 785 ranked configurations on 25 Oct 2026. 289 other configurations score within its margin, so the exact position matters less than the band. The leader is Claude Opus 5.5 (max effort) at 72.6.
- How much does Qwen2.5 7B Instruct cost per 1M tokens?
- As of 25 Oct 2026, Qwen2.5 7B Instruct costs $0.100 per 1M input tokens and $0.200 per 1M output tokens, $0.125 blended (three input tokens per output token). The list price last changed on 23 Oct 2026.
- When was Qwen2.5 7B Instruct released?
- Alibaba released Qwen2.5 7B Instruct on 19 Sept 2024.
Data as of 25 Oct 2026. Compare with another model.
Cite as: BenchLeader, “Qwen2.5 7B Instruct: benchmarks, pricing, speed and rank”, data as of 25 Oct 2026, https://www.benchleader.com/models/qwen2-5-7b.