Last verified 2026-02-11 (left) · 2026-03-12 (right)

Claude Haiku 4.5 vs Qwen Plus — Pricing & Capability Comparison

Claude Haiku 4.5 charges $1.00 per million input tokens and $5.00 per million output tokens. Qwen Plus comes in at $0.40 / $4.00. Context windows span 200K vs 256K tokens respectively.

TL;DR — Quick Comparison

✓Qwen Plus is cheaper overall: $4.40 per 1M tokens (in+out) vs $6.00 for Claude Haiku 4.5 — saves $1.60 per 1M tokens
✓Input pricing: Claude Haiku 4.5 $1.00/1M vs Qwen Plus $0.40/1M
✓Output pricing: Claude Haiku 4.5 $5.00/1M vs Qwen Plus $4.00/1M
✓Context window: Qwen Plus offers more (256K vs 200K)
✓Use our calculator below to estimate costs for your specific usage pattern

Input price (per 1M)

Claude Haiku 4.5

$1.00

Qwen Plus

$0.40

Qwen Plus leads here

Output price (per 1M)

Claude Haiku 4.5

$5.00

Qwen Plus

$4.00

Qwen Plus leads here

Context window

Claude Haiku 4.5

200,000 tokens

Qwen Plus

256,000 tokens

Qwen Plus leads here

Cached input

Claude Haiku 4.5

$0.100

Qwen Plus

Not published

Claude Haiku 4.5 leads here

Which one should you choose?

Skip the spreadsheet if you just need the practical takeaway. Use these rules when deciding between Claude Haiku 4.5 and Qwen Plus.

Choose Qwen Plus if input tokens dominate your bill

Qwen Plus has the lower input rate, which usually matters most for chat, RAG, classification, and long-prompt workflows where prompt volume stays much larger than generated output.

Choose Qwen Plus if you generate long answers

Qwen Plus is cheaper on output tokens, so it tends to win for report generation, coding assistance, reasoning traces, and any workflow where completions are long.

Choose Qwen Plus if context size is the blocker

Qwen Plus offers the larger published context window, which is more important than small pricing differences when you need to fit large files, long chats, or multi-document prompts into one request.

Cost comparison for 10K-token workloads

Side-by-side pricing for identical workloads (10,000 total tokens per request) across different distributions.

Scenario	Claude Haiku 4.5	Qwen Plus	Claude Haiku 4.5 cached
Balanced conversation 50% input · 50% output	$0.0300	$0.0220	$0.0255
Input-heavy workflow 80% input · 20% output	$0.0180	$0.0112	$0.0108
Generation heavy 30% input · 70% output	$0.0380	$0.0292	$0.0353
Cached system prompt 90% cached input · 10% fresh output	$0.0140	$0.0076	$0.0059

Frequently asked questions

Which is cheaper: Claude Haiku 4.5 or Qwen Plus?

Qwen Plus is cheaper for input tokens at $0.40 per 1M tokens compared to $1.00. For output, Qwen Plus costs $4.00 per 1M tokens versus $5.00 for Claude Haiku 4.5.

What is the cost per 1M tokens for Claude Haiku 4.5?

Claude Haiku 4.5 pricing: $1.00 per 1M input tokens and $5.00 per 1M output tokens. Context window: 200,000 tokens.

What is the cost per 1M tokens for Qwen Plus?

Qwen Plus pricing: $0.40 per 1M input tokens and $4.00 per 1M output tokens. Context window: 256,000 tokens.

How much does it cost per 1K tokens?

Per 1K tokens: Claude Haiku 4.5 costs $0.0010 input / $0.0050 output. Qwen Plus costs $0.0004 input / $0.0040 output. This is useful for calculating small-scale usage costs.

Which model supports a larger context window?

Qwen Plus offers 256,000 tokens (256K) versus 200K for Claude Haiku 4.5.

What is the estimated monthly cost for typical usage?

For a typical workload of 10M input + 2M output tokens per month: Claude Haiku 4.5 would cost approximately $20.00, while Qwen Plus would cost $12.00. Qwen Plus is more economical for this usage pattern.

Do these models support prompt caching?

Claude Haiku 4.5 supports prompt caching at $0.100 per 1M cached tokens, reducing costs for repeated context by up to 90%. Qwen Plus does not publish cached pricing.

Which model is best for my use case?

Choose Qwen Plus for cost-sensitive applications with high input volume. Choose Qwen Plus if you need 256K context for long documents or conversations. Consider prompt caching if you have repeated context. Use our token calculator to model your specific usage pattern.