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Qwen3.8 Flash vs Beam 1.0 501B A23B

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

Qwen3.8 Flash is the cheaper of the two; neither can be ranked on quality here.

Beam 1.0 501B A23B does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.

qwen

Qwen3.8 Flash

Blended / 1M
$0.230
Context
1M
Released
Aug 26, 2026
Overall score
61.9
reasoningtool callingimage inputvideo inputprompt caching

reflection

Beam 1.0 501B A23B

Blended / 1M
$0.438
Context
1.0M
Released
Oct 16, 2026
Overall score
Not evaluated
reasoningtool calling

Specs and pricing

MetricQwen3.8 FlashBeam 1.0 501B A23B
LiveBench overall

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

61.9—
Cost per point

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

$0.6321—
Blended price / 1M

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

$0.230win$0.438
Input price / 1M$0.150win$0.250
Output price / 1M$0.470win$1.00
Cached input / 1M

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

$0.016—
Context window1M1.0M
Max output tokens131K131K

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Qwen3.8 Flash on top, Beam 1.0 501B A23B below, both out of 100.

Coding
87.4
—
Reasoning
87.4
—
Data analysis
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.

WorkloadQwen3.8 FlashBeam 1.0 501B A23B
Support chatbot

1.2K in / 400 out × 200K requests

$63.95/mo$140.00/mo
RAG assistant

8K in / 600 out × 100K requests

$94.60/mo$260.00/mo
Coding agent

40K in / 4K out × 20K requests

$82.56/mo$280.00/mo
Document extraction

20K in / 1.5K out × 50K requests

$178.55/mo$325.00/mo
Bulk classification

500 in / 20 out × 5M requests

$355.00/mo$725.00/mo
Run these two through the cost calculator

Which should you pick?

You are cost-constrained

Qwen3.8 Flash

Cheaper on blended list price at $0.230 per million tokens.

Qwen3.8 Flash vs Beam 1.0 501B A23B FAQ

Which is better, Qwen3.8 Flash or Beam 1.0 501B A23B?

Qwen3.8 Flash is the cheaper of the two; neither can be ranked on quality here. Beam 1.0 501B A23B does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.

Is Qwen3.8 Flash cheaper than Beam 1.0 501B A23B?

Qwen3.8 Flash is cheaper. On a 3:1 input:output blend, Qwen3.8 Flash lists at $0.230 per million tokens and Beam 1.0 501B A23B at $0.438 — Qwen3.8 Flash is 1.9× cheaper. Input and output are priced separately — Qwen3.8 Flash charges $0.150 in and $0.470 out, Beam 1.0 501B A23B charges $0.250 and $1.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.

Does Qwen3.8 Flash or Beam 1.0 501B A23B have a bigger context window?

They are effectively the same — 1M for Qwen3.8 Flash and 1.0M for Beam 1.0 501B A23B.

Do Qwen3.8 Flash and Beam 1.0 501B A23B support prompt caching?

Qwen3.8 Flash publishes a cached-input rate of $0.016 per million tokens against a full input rate of $0.150. The catalogue lists no separate cached rate for Beam 1.0 501B A23B, which means the provider does not price it separately here — not that caching is unavailable.

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.