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Claude Haiku 4.5 vs Qwen3.8 Flash

Published LiveBench scores across all seven categories, live list pricing, context windows, and the measured cost of a point of capability — for both models, side by side.

OpenRouter + LiveBenchAll comparisonsFull leaderboard

Effectively the same quality — Qwen3.8 Flash is the cheaper way to get it.

The two are within 0.9 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. Qwen3.8 Flash lists 8.7× cheaper per blended million tokens. When quality ties, cost is the whole decision. Qwen3.8 Flash also leads on measured cost per point of capability, at $0.2009 per point.

anthropic

Claude Haiku 4.5

Blended / 1M
$2.00
Context
200K
Released
Oct 15, 2025
Overall score
46.0
reasoningtool callingimage inputfile inputprompt caching

qwen

Qwen3.8 Flash

Blended / 1M
$0.230
Context
1M
Released
Aug 26, 2026
Overall score
47.0
reasoningtool callingimage inputvideo inputprompt caching

Specs and pricing

MetricClaude Haiku 4.5Qwen3.8 Flash
LiveBench overall

Mean of the seven LiveBench category scores, 0–100. Higher is better.

46.047.0
Cost per point

Measured benchmark spend divided by overall score — dollars per point of capability.

$0.3893$0.2009win
Blended price / 1M

3:1 input:output mix, the usual shape of production traffic.

$2.00$0.230win
Input price / 1M$1.00$0.150win
Output price / 1M$5.00$0.470win
Cached input / 1M

Price of an input token served from the prompt cache, where the provider publishes one.

$0.100$0.016win
Context window200K1Mwin
Max output tokens64K131Kwin

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Claude Haiku 4.5 on top, Qwen3.8 Flash below, both out of 100.

Coding
48.1
53.5
Reasoning
61.7
87.4
Data analysis
59.3
74.2

What each one costs to run

Per-token prices are hard to feel. These are monthly list costs for both models across five workload shapes, using each provider's published cached-input rate where there is one.

WorkloadClaude Haiku 4.5Qwen3.8 Flash
Support chatbot

1.2K in / 400 out × 200K requests

$575.20/mo$63.95/mo
RAG assistant

8K in / 600 out × 100K requests

$740.00/mo$94.60/mo
Coding agent

40K in / 4K out × 20K requests

$696.00/mo$82.56/mo
Document extraction

20K in / 1.5K out × 50K requests

$1,330/mo$178.55/mo
Bulk classification

500 in / 20 out × 5M requests

$2,550/mo$355.00/mo
Run these two through the cost calculator

Which should you pick?

You are running this at volume

Qwen3.8 Flash

Lowest measured cost per point of capability at $0.2009 per point — the gap compounds with every request.

The workload is coding or agentic work

Qwen3.8 Flash

Leads on coding — 53.5 against 48.1.

You need to fit large documents in one call

Qwen3.8 Flash

Wider context window — 1M against 200K.

Claude Haiku 4.5 vs Qwen3.8 Flash FAQ

Which is better, Claude Haiku 4.5 or Qwen3.8 Flash?

Effectively the same quality — Qwen3.8 Flash is the cheaper way to get it. The two are within 0.9 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. Qwen3.8 Flash lists 8.7× cheaper per blended million tokens. When quality ties, cost is the whole decision. Qwen3.8 Flash also leads on measured cost per point of capability, at $0.2009 per point.

Is Claude Haiku 4.5 cheaper than Qwen3.8 Flash?

Qwen3.8 Flash is cheaper. On a 3:1 input:output blend, Claude Haiku 4.5 lists at $2.00 per million tokens and Qwen3.8 Flash at $0.230 — Qwen3.8 Flash is 8.7× cheaper. Input and output are priced separately — Claude Haiku 4.5 charges $1.00 in and $5.00 out, Qwen3.8 Flash charges $0.150 and $0.470 — so the model that looks cheaper flips depending on how output-heavy your workload is.

Claude Haiku 4.5 vs Qwen3.8 Flash: which scores higher on benchmarks?

Claude Haiku 4.5 scores 46.0 and Qwen3.8 Flash scores 47.0 overall on LiveBench, the mean of its seven categories. That gap is inside the range that effort settings alone move a score, so treat them as equivalent on published quality. Category scores differ from the overall figure — a model can lead on reasoning and trail on coding, which the per-category table above breaks out.

Which gives better value for money, Claude Haiku 4.5 or Qwen3.8 Flash?

Qwen3.8 Flash. Cost per point divides the measured dollars LiveBench spent running the benchmark by the score it earned, so it captures something token pricing misses: a reasoning model can emit many times more tokens than its per-token rate suggests. Claude Haiku 4.5 works out at $0.3893 per point and Qwen3.8 Flash at $0.2009.

Does Claude Haiku 4.5 or Qwen3.8 Flash have a bigger context window?

Qwen3.8 Flash has the larger context window: 200K for Claude Haiku 4.5 against 1M for Qwen3.8 Flash. Note that a window you can fill is not a window you should fill — retrieval quality usually degrades well before the limit, and you pay for every token you put in it.

Do Claude Haiku 4.5 and Qwen3.8 Flash support prompt caching?

Both publish a cached-input rate: $0.100 per million for Claude Haiku 4.5 and $0.016 for Qwen3.8 Flash, against full input rates of $1.00 and $0.150. On a workload with a long stable prefix — a system prompt, a tool schema, a retrieved corpus — that changes the economics more than the headline price does.

Related comparisons

How these numbers are produced

  • Price — provider list price from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
  • Scores — LiveBench release 2026-09-02, using their own category map. Each model shows its strongest published run. A blank means “not evaluated”, never “bad”.
  • Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
  • “Win” — awarded only past a threshold: one full point on a benchmark score, 10% on a price, 25% on a context window. Anything tighter reports as a tie, because effort settings alone move a LiveBench score by more than that.

Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.