Model profile
IBM
New in 2026

IBM: Granite 4.2 8B

IBM: Granite 4.2 8B is a budget-priced text-first model from IBM with fast runtime profile, extended context posture, and the clearest fit around long-context research / agent workflows.

Best for: Long-context research / Agent workflowsFast latencyExtended contextBudget pricing
Intelligence
19.6

Benchmark blend

Coding
22.4

Dev workflow signal

Context
131K Tokens

Extended

Input Price
$0.06

Budget tier

Decision snapshot
54

IBM: Granite 4.2 8B currently reads as a budget text-first option with extended context and a fast runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Agent workflows
Latency tier
Fast
Price tier
Budget
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
19.6
20

General reasoning and benchmark headroom.

Limited
Speed
144 tok/s
100

TTFT 0.23s

Above average
Context
131K Tokens
76

How much prompt and task state can stay in view.

Competitive
Price
$0.06
86

$0.25 output / 1M

Efficient

Editorial Profile

IBM: Granite 4.2 8B 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 22Math score N/A

Granite 4.2 8B is a dense reasoning model from IBM. It is suited for mathematics, code generation, multilingual dialogue, and agentic workflows that need multi-step reasoning. It supports full, low-effort,...

Identity

IBM text-first profile

Positioning

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

Cost posture

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

Strengths
  • Large context headroom supports repo-wide prompts and long research sessions.

  • 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
  • Long-context summarization, repo analysis, and policy or document review.

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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
19.6
GPQA
63.1%
HLE
9.7%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
22.4
SciCode
30.4%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

LCR
43.3%

Extra benchmark cuts are not available for this category yet.

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
131K Tokens
Vision
Text-first
Modalities
text
Tokenizer
Other
Max Completion
117964
Moderation
No
Supported Parameters
frequency_penaltyinclude_reasoninglogit_biaslogprobsmax_tokensmin_ppresence_penaltyreasoningreasoning_effortrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_logprobstop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.06
Output
per 1M output tokens
$0.25
Blended
AA 3:1 mix
$0.11

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

OR Cache Read
$0.00

Metadata

Raw source tables at the end

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