LLM Reference

Qwen2.5-1.5B-Instruct

Released
2024-06-07
Last refreshed
2026-04-15
Status
Researched 154d ago
CodingLong contextClassification

Qwen2.5-1.5B-Instruct is worth evaluating for coding, long context, and classification when its provider route and context window match the workload.

Use it for

  • Teams evaluating coding, long context, and classification
  • Workloads that can use a 128k context window
  • Buyers comparing 1 tracked provider route

Do not use it for

  • Vision or document-understanding workloads
  • Strict JSON or tool-calling flows
Specifications
Family
Qwen2.5
Released
2024-06-07
Context
128k
Parameters
1.54B
Architecture
Decoder Only
Specialization
general
Training
finetuned
Fine-tuning
instruct
Created by

AI research institute of Alibaba Group.

Hangzhou, Zhejiang, China
Founded 2017
Website
Pricing
Output / 1M
$0.100
Input / 1M
$0.100

Cheapest of 1 route · Fireworks AI

About

Instruction-optimized 1.5B variant for conversational AI and task automation on consumer hardware with strong multilingual support.

Qwen2.5-1.5B-Instruct is a model in the Qwen2.5 family. The structured metadata tracks a 128k-token context window. This page tracks provider routes through Fireworks AI, with the cheapest tracked route listed at $0.1 input and $0.1 output per 1M tokens. Headline tracked benchmarks include Google-Proof Q&A 34.8, HellaSwag 84.5, and HumanEval 48.9.

Top use-case fit: coding, agents, and build tasks

Coding

Q/$ A

1 relevant benchmark in the decision map.

Long context

Included by capability and metadata signals in the decision map.

Classification

Q/$ A

2 relevant benchmarks in the decision map.

Provider price ladder

Compare API pricing across 1 providers for input and output tokens, batch, and cached reads when available.

ProviderInput / 1MOutput / 1MRoute
Fireworks AI$0.100$0.100
Serverless

Capabilities

No model capability flags are currently sourced.

Benchmark peer barsfor Coding

Benchmark scores(4)

Scores are benchmark-specific and are direction-aware: the same numeric gap can mean very different outcomes across suites. Use the leaderboard context and this model's provider route to decide whether the winning margin is meaningful for your workload.
BenchmarkScoreVersionSource
Google-Proof Q&A34.8diamondhttps://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
HellaSwag84.510-shothttps://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
HumanEval48.9pass@1https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
Massive Multitask Language Understanding61.25-shothttps://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard

Migration checks

No linked migration route is available for this model yet.

Rankings & picks(10)