Model profile
AI21

Jamba 1.5 Large

Jamba 1.5 Large is a mid-range text-first model from AI21 with a heavy runtime profile, unknown context posture, and the clearest fit around agent workflows / long-context research.

Best for: Agent workflows / Long-context researchHeavy latencyUnknown contextMid-range pricing
Intelligence
10.7

Benchmark blend

Coding
0.143

Dev workflow signal

Context
N/A

Unknown

Input Price
$2.00

Mid-range tier

Decision snapshot
30

Jamba 1.5 Large currently reads as a mid-range text-first option with unknown context and a heavy runtime profile.

Overall profile
Use-case specific
Best for
Agent workflows / Long-context research
Latency tier
Heavy
Price tier
Mid-range
Source coverage
OpenRouterArtificial Analysis

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
10.7
11

General reasoning and benchmark headroom.

Limited
Speed
N/A
46

Latency data is partial.

Situational
Context
N/A
34

How much prompt and task state can stay in view.

Limited
Price
$2.00
62

$8.00 output / 1M

Competitive

Editorial Profile

Jamba 1.5 Large in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Use-case specificCoding score 14Math score 61

The Jamba 1.5 Large AI model by AI21.

Identity

AI21 text-first profile

Positioning

Agent workflows / Long-context research with unknown context and heavy runtime.

Cost posture

Balanced spend profile. Easier to justify in mixed production and exploration workloads.

Strengths
  • The available source data suggests a balanced profile rather than one dominant edge.

Tradeoffs
  • 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.

Best fit
  • Focused chat, retrieval-augmented flows, and narrower production tasks.

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Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
10.7
MMLU Pro
57.2%
GPQA
42.7%
HLE
4.0%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.143
SciCode
16.3%
Math

Formal reasoning, structured problem solving, and competition-style math.

AIME
4.7%
Math 500
60.6%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
N/A
Vision
Text-first
Price architecture
Input
per 1M input tokens
$2.00
Output
per 1M output tokens
$8.00
Blended
AA 3:1 mix
$3.50

This model sits in a balanced spend range. It is easier to justify across both production and exploratory workflows.

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.