Intelligence
15.0
15
General reasoning and benchmark headroom.
LimitedQwen3 30B A3B 2507 Instruct is a budget-priced text-first model from Alibaba with fast runtime profile, partial context coverage, and the clearest fit around agent workflows / reasoning.
Benchmark blend
Dev workflow signal
N/A
Budget tier
Qwen3 30B A3B 2507 Instruct currently reads as a budget text-first option with partially published context and a fast runtime profile.
Decision Strip
Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.
General reasoning and benchmark headroom.
LimitedTTFT 0.93s
CompetitiveHow much prompt and task state can stay in view.
Unavailable$0.80 output / 1M
EfficientEditorial Profile
Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.
The Qwen3 30B A3B 2507 Instruct AI model by Alibaba.
Alibaba text-first profile
Agent workflows / Reasoning with partially published context and fast runtime.
Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.
Latency and throughput look responsive enough for interactive loops.
Budget-friendly input pricing is a strength, but raw capability may vary by workload.
Current metadata points to a text-first profile rather than a broad multimodal one.
Context limits are only partially published, so long-session planning needs extra validation.
Focused chat, retrieval-augmented flows, and narrower production tasks.
Benchmarks
Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.
Broad reasoning, knowledge depth, and flagship benchmark posture.
Software implementation, debugging quality, and coding benchmark signal.
Formal reasoning, structured problem solving, and competition-style math.
Long-horizon execution quality and interactive benchmark evidence.
Specs & Pricing
Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.
This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.
Metadata
Verification details remain available, but the page no longer forces them ahead of the editorial read.