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CostPerPrompt

Qwen3 30B A3B Instruct 2507 API Pricing

Alibaba (Qwen) · context window 262K · prices updated 2026-10-30

Input per 1M tokens
$0.1
Output per 1M tokens
$0.3
Cached input per 1M
$0.1
0% cheaper than fresh input

Where Qwen3 30B A3B Instruct 2507 sits on price

At $0.3 per million output tokens, Qwen3 30B A3B Instruct 2507 is a budget tier model — 7.3× cheaper than the median output price of $2.20, which puts it cheaper than 88% and pricier than 12% of the models we track. It sits 8th cheapest of the 42 Alibaba (Qwen) models we track. Output is only 3.0× the input price, an unusually flat spread: long prompts hurt about as much as long answers, so prompt size is where the savings are.

Output tokens per $1
3,333,333
One full context fill
$0.0262
Cheaper than
88% of tracked models

What real workloads cost on Qwen3 30B A3B Instruct 2507

These three workloads are the ones teams actually run on a budget-priced model — a $0.3/1M model is not bought for the same job as one ten times the price.

Workload Per request Per month
High-volume classification — 200K requests/day (400 in / 20 out) Short output, huge volume: the input price dominates the bill. $0 $276
Small app — 1K requests/day (500 in / 150 out) The baseline most side projects actually run at. $0.0001 $2.85
Document pipeline — 10K docs/day (3K in / 500 out) Batch-friendly: no user waiting, so a ~50% batch discount usually applies. $0.0004 $135

Model your exact traffic in the API cost calculator — it preloads Qwen3 30B A3B Instruct 2507 with caching and batch options.

Qwen3 30B A3B Instruct 2507 price history

Alibaba (Qwen) last raised Qwen3 30B A3B Instruct 2507's output price on Oct 26, 2026, from $0.09/$0.3 to $0.1/$0.3 per 1M in/out. That is one of 9 repricings in the 89 days we have tracked it — worth knowing before you hard-code today's rate into a budget.

Date Input /1M Output /1M Output change
Aug 2, 2026 (tracking began) $0.0481 $0.193 —
Sep 10, 2026 $0.09 $0.3 +55%
Sep 13, 2026 $0.0481 $0.193 -36%
Sep 24, 2026 $0.1 $0.3 +55%
Sep 29, 2026 $0.0481 $0.193 -36%
Oct 6, 2026 $0.1 $0.3 +55%
Oct 7, 2026 $0.0481 $0.193 -36%
Oct 9, 2026 $0.1 $0.3 +55%
Oct 18, 2026 $0.09 $0.3 0%
Oct 26, 2026 $0.1 $0.3 0%

Change-points from our daily price snapshots (tracking since Aug 2, 2026; intraday moves between snapshots are not captured).

Qwen3 30B A3B Instruct 2507 vs Gemma 4 26B A4B

The closest-priced alternative from another vendor is Gemma 4 26B A4B (Google) — priced within a rounding error on output, with $0.01 less per million input tokens. When two models land this close on price, the decision is quality on your own workload, not the price sheet: run 50 real requests through both and compare.

See Gemma 4 26B A4B pricing →

Cheaper alternatives

More Alibaba (Qwen) models

Frequently asked questions

› How much does the Qwen3 30B A3B Instruct 2507 API cost?

Qwen3 30B A3B Instruct 2507 costs $0.1 per million input tokens and $0.3 per million output tokens, with cached input at $0.1 per million (0% cheaper). That works out to roughly 3,333,333 output tokens per dollar.

› What does the high-volume classification workload cost on Qwen3 30B A3B Instruct 2507?

High-volume classification — 200K requests/day (400 in / 20 out) costs about $0 per request and $276 per month on Qwen3 30B A3B Instruct 2507. Short output, huge volume: the input price dominates the bill.

› What does it cost to fill Qwen3 30B A3B Instruct 2507's 262K context window?

Sending 262K of input in a single request costs $0.0262 at $0.1 per million tokens — before any output. There is no cached-input tier on this model, so every repeat of that context is billed at full price. This is why large context windows are cheap to advertise and expensive to actually use.

› Is Qwen3 30B A3B Instruct 2507 worth the price?

Qwen3 30B A3B Instruct 2507 is in the cheapest end of the market — cheaper than 88% of the models we track. At this price the question is not cost but capability: it is a good fit for classification, extraction, routing and bulk summarisation, and a poor fit for multi-step reasoning where a wrong answer costs more than the tokens saved.