Intelligence
10.7
11
General reasoning and benchmark headroom.
LimitedJamba 1.5 Large is a mid-range-priced text-first model from AI21 Labs with partial runtime data, partial context coverage, and the clearest fit around coding / reasoning.
Benchmark blend
Dev workflow signal
N/A
Mid-range tier
Jamba 1.5 Large currently reads as a mid-range text-first option with partially published context and a partially published 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.
UnavailableHow much prompt and task state can stay in view.
Unavailable$8.00 output / 1M
CompetitiveEditorial Profile
Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.
The Jamba 1.5 Large AI model by AI21 Labs.
AI21 Labs text-first profile
Coding / Reasoning with partially published context and partially published 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 data is incomplete, so interactive responsiveness is harder to rank confidently.
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.
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.