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CostPerPrompt

Qwen3 235B A22B Instruct 2507 API Pricing

Alibaba (Qwen) · context window 262K · prices updated 2026-09-15

Input per 1M tokens
$0.0875
Output per 1M tokens
$0.35
Cached input per 1M
$0.0175
80% cheaper than fresh input

Where Qwen3 235B A22B Instruct 2507 sits on price

At $0.35 per million output tokens, Qwen3 235B A22B Instruct 2507 is a budget tier model — 5.9× cheaper than the median output price of $2.08, which puts it cheaper than 85% and pricier than 15% of the models we track. It sits 10th cheapest of the 53 Alibaba (Qwen) models we track. Output costs 4.0× input, the usual spread — trim both, starting with the answer length.

Output tokens per $1
2,857,143
One full context fill
$0.0229
Cheaper than
85% of tracked models

Cached input is 80% cheaper. On an input-heavy workload you need roughly a 31% cache-hit rate to take 25% off the input line — reachable for chatbots and agents that resend the same system prompt and history.

What real workloads cost on Qwen3 235B A22B Instruct 2507

These three workloads are the ones teams actually run on a budget-priced model — a $0.35/1M 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 $252
Small app — 1K requests/day (500 in / 150 out) The baseline most side projects actually run at. $0.0001 $2.89
Document pipeline — 10K docs/day (3K in / 500 out) Batch-friendly: no user waiting, so a ~50% batch discount usually applies. $0.0004 $131

Model your exact traffic in the API cost calculator — it preloads Qwen3 235B A22B Instruct 2507 with caching and batch options.

Qwen3 235B A22B Instruct 2507 price history

Alibaba (Qwen) last cut Qwen3 235B A22B Instruct 2507's output price on Sep 12, 2026 (-60% per output token), from $0.22/$0.88 to $0.0875/$0.35 per 1M in/out. That is one of 6 repricings in the 44 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) $0.09 $0.55
Aug 3, 2026 $0.1495 $0.598 +9%
Aug 6, 2026 $0.09 $0.55 -8%
Aug 28, 2026 $0.0875 $0.35 -36%
Sep 5, 2026 $0.09 $0.55 +57%
Sep 9, 2026 $0.22 $0.88 +60%
Sep 12, 2026 $0.0875 $0.35 -60%

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

Qwen3 235B A22B Instruct 2507 vs Gemma 4 31B

The closest-priced alternative from another vendor is Gemma 4 31B (Google) — 3% cheaper on output, with $0.0025 more 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 31B pricing →

Cheaper alternatives

More Alibaba (Qwen) models

Frequently asked questions

How much does the Qwen3 235B A22B Instruct 2507 API cost?

Qwen3 235B A22B Instruct 2507 costs $0.0875 per million input tokens and $0.35 per million output tokens, with cached input at $0.0175 per million (80% cheaper). That works out to roughly 2,857,143 output tokens per dollar.

What does the high-volume classification workload cost on Qwen3 235B A22B Instruct 2507?

High-volume classification — 200K requests/day (400 in / 20 out) costs about $0 per request and $252 per month on Qwen3 235B A22B Instruct 2507. Short output, huge volume: the input price dominates the bill.

What does it cost to fill Qwen3 235B A22B Instruct 2507's 262K context window?

Sending 262K of input in a single request costs $0.0229 at $0.0875 per million tokens — before any output. With prompt caching that same fill drops to about $0.0046 on repeat requests. This is why large context windows are cheap to advertise and expensive to actually use.

Is Qwen3 235B A22B Instruct 2507 worth the price?

Qwen3 235B A22B Instruct 2507 is in the cheapest end of the market — cheaper than 85% 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.