Qwen 2.5 Coder 32B: token counter & pricing
Alibaba · approximate, within ±3% of reference · pricing as of 2026-04-26.
- Provider
- Alibaba
- API model ID
qwen2.5-coder-32b-instruct- Context window
- 131,072 tokens
- Input price
- $0.80 per 1M tokens
- Output price
- $0.80 per 1M tokens
- Tokenizer accuracy
- approximate, within ±3% of reference
- Pricing as of
- 2026-04-26
Open the counter to count tokens for Qwen 2.5 Coder 32B in real time.
What is Qwen 2.5 Coder 32B?
Qwen 2.5 Coder 32B is Alibaba's code-specialized open-weights model — 32 billion parameters, 131k context, the strongest open-weights coding model available as of early 2026. Beats Llama 3.1 70B on code benchmarks despite being less than half the size.
How tokens are counted here
Same Qwen 2.5 tokenizer as the general 72B model. Browser approximation, accurate to ~±3%. Marked ≈±3%.
Code tokenization tends to be slightly less efficient than prose tokenization across all models — expect ~10-20% more tokens for the same character count of code than equivalent prose.
When to use Qwen 2.5 Coder
- Self-hosted code generation — runs on a single 24GB GPU when quantized, single 80GB unquantized.
- Coding agents and IDE integrations where you want an open-weights model that doesn't send your code to a vendor.
- Cost-sensitive coding workloads at high volume — much cheaper than Claude Sonnet or GPT-4o for code.
- Fine-tuning for domain-specific code styles (your codebase's conventions, internal frameworks).
When not to use it:
- Architecture-class problems requiring multi-step reasoning over large codebases. Claude Opus or Sonnet still lead here.
- Workloads needing reliable function-calling / structured outputs — OpenAI's tooling is more mature.
- Production workloads where you need a vendor's SLA and support.
Pricing notes
At $0.80 per million (single rate via Together.ai), Coder 32B is roughly 3× cheaper than Claude Sonnet on input and 19× cheaper on output for code generation tasks.
Common questions
Is Qwen Coder really better than Llama 70B on code?
On published coding benchmarks (HumanEval, MBPP, BigCodeBench), yes — meaningfully better despite being 38% the size. On real-world IDE integration and agent workflows, the gap narrows; depends heavily on prompt style and language.
What languages does it cover?
Strong on Python, JavaScript/TypeScript, Java, C++, Go, Rust. Decent on most other major languages. Multilingual code comments work well in English and CJK.
Can I run this on a Mac?
Yes — the 4-bit quantized version runs in 24GB of unified memory. An M2 Pro or M3 Pro Mac with 32GB+ RAM via Ollama or LM Studio is comfortable for interactive use.
Compare Qwen 2.5 Coder 32B to other models
- Qwen 2.5 72B (Alibaba, $0.90/$0.90)
- Claude Haiku 4.5 (Anthropic, $0.80/$4.00)
- Llama 3.1 70B (Meta, $0.59/$0.79)
- Gemini 2.5 Pro (Google, $1.25/$10.00)