Intelligence
12.0
12
General reasoning and benchmark headroom.
LimitedQwen: Qwen-Turbo is a budget-priced text-first model from Alibaba with balanced runtime profile, extended context posture, and the clearest fit around long-context research / coding.
Benchmark blend
Dev workflow signal
Extended
Budget tier
Qwen: Qwen-Turbo currently reads as a budget text-first option with extended context and a balanced 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 1.01s
CompetitiveHow much prompt and task state can stay in view.
Competitive$0.20 output / 1M
EfficientEditorial Profile
Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.
Qwen-Turbo, based on Qwen2.5, is a 1M context model that provides fast speed and low cost, suitable for simple tasks.
Alibaba text-first profile
Long-context research / Coding with extended context and balanced runtime.
Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.
Large context headroom supports repo-wide prompts and long research sessions.
Budget-friendly input pricing is a strength, but raw capability may vary by workload.
Latency is balanced rather than ultra-fast, which is fine for most workflows but not the snappiest tier.
Current metadata points to a text-first profile rather than a broad multimodal one.
Long-context summarization, repo analysis, and policy or document review.
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.
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.