Model profile
Google
New in 2026

Gemma 4 31B (Reasoning)

Gemma 4 31B (Reasoning) is a budget-priced text-first model from Google with balanced runtime profile, partial context coverage, and the clearest fit around agent workflows / coding.

Best for: Agent workflows / CodingBalanced latencyN/A contextBudget pricing
Intelligence
29.7

Benchmark blend

Coding
43.4

Dev workflow signal

Context
N/A

N/A

Input Price
$0.00

Budget tier

Decision snapshot
48

Gemma 4 31B (Reasoning) currently reads as a budget text-first option with partially published context and a balanced runtime profile.

Overall profile
Use-case specific
Best for
Agent workflows / Coding
Latency tier
Balanced
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
29.7
30

General reasoning and benchmark headroom.

Limited
Speed
35 tok/s
53

TTFT 1.03s

Situational
Context
N/A
N/A

How much prompt and task state can stay in view.

Unavailable
Price
$0.00
86

$0.00 output / 1M

Efficient

Editorial Profile

Gemma 4 31B (Reasoning) 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 43Math score N/A

The Gemma 4 31B (Reasoning) AI model by Google.

Identity

Google text-first profile

Positioning

Agent workflows / Coding with partially published context and balanced runtime.

Cost posture

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

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

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Latency is balanced rather than ultra-fast, which is fine for most workflows but not the snappiest tier.

  • 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.

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
29.7
GPQA
85.7%
HLE
23.6%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
43.4
SciCode
43.4%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
75.6%
TAU2
59.9%
TerminalBench Hard
36.4%
LCR
68.3%

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
$0.00
Output
per 1M output tokens
$0.00
Blended
AA 3:1 mix
$0.00

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.