Intelligence
14.2
14
General reasoning and benchmark headroom.
LimitedClaude 3.5 Sonnet (June '24) is a mid-range text-first model from Anthropic with a heavy runtime profile, unknown context posture, and the clearest fit around agent workflows / long-context research.
Benchmark blend
Dev workflow signal
Unknown
Mid-range tier
Claude 3.5 Sonnet (June '24) currently reads as a mid-range text-first option with unknown context and a heavy 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.
LimitedLatency data is partial.
SituationalHow much prompt and task state can stay in view.
Limited$15.00 output / 1M
CompetitiveEditorial Profile
Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.
The Claude 3.5 Sonnet (June '24) AI model by Anthropic.
Anthropic text-first profile
Agent workflows / Long-context research with unknown context and heavy runtime.
Balanced spend profile. Easier to justify in mixed production and exploration workloads.
The available source data suggests a balanced profile rather than one dominant edge.
Costs look manageable, but still deserve attention in always-on agents or batch jobs.
Latency profile is better for deliberate runs than rapid back-and-forth chat.
Current metadata points to a text-first profile rather than a broad multimodal one.
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.
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 sits in a balanced spend range. It is easier to justify across both production and exploratory workflows.
Metadata
Verification details remain available, but the page no longer forces them ahead of the editorial read.