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Model profile
AI21

Jamba 1.6 Mini

Jamba 1.6 Mini is a budget text-first model from AI21 with a fast runtime profile, unknown context posture, and the clearest fit around agent workflows / long-context research.

Best for: Agent workflows / Long-context researchFast latencyUnknown contextBudget pricing
Intelligence
7.9

Benchmark blend

Coding
0.071

Dev workflow signal

Context
N/A

Unknown

Input Price
$0.20

Budget tier

Decision snapshot
40

Jamba 1.6 Mini currently reads as a budget text-first option with unknown context and a fast runtime profile.

Overall profile
Use-case specific
Best for
Agent workflows / Long-context research
Latency tier
Fast
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis
Compare this modelSee leaderboard

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
7.9
8

General reasoning and benchmark headroom.

Limited
Speed
183 tok/s
96

TTFT 0.68s

Above average
Context
N/A
34

How much prompt and task state can stay in view.

Limited
Price
$0.20
86

$0.40 output / 1M

Efficient

Editorial Profile

Jamba 1.6 Mini 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 7Math score 26

The Jamba 1.6 Mini AI model by AI21.

Identity

AI21 text-first profile

Positioning

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

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths

  • Latency and throughput look responsive enough for interactive loops.

Tradeoffs

  • 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.

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
7.9
MMLU Pro
36.7%
GPQA
30.0%
HLE
4.6%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.071
SciCode
10.1%
Math

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

AIME
3.3%
Math 500
25.7%

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
$0.20
Output
per 1M output tokens
$0.40
Blended
AA 3:1 mix
$0.25

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

Metadata

Raw source tables at the end

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