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Model profile
Google

Gemini 1.0 Pro

Gemini 1.0 Pro is a budget text-first model from Google 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 contextBudget pricing
Intelligence
8.5

Benchmark blend

Coding
0.116

Dev workflow signal

Context
N/A

Unknown

Input Price
$0.00

Budget tier

Decision snapshot
32

Gemini 1.0 Pro currently reads as a budget 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
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
8.5
9

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
$0.00
86

$0.00 output / 1M

Efficient

Editorial Profile

Gemini 1.0 Pro 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 12Math score 40

The Gemini 1.0 Pro AI model by Google.

Identity

Google text-first profile

Positioning

Agent workflows / Long-context research with unknown context and heavy 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 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
8.5
MMLU Pro
43.1%
GPQA
27.7%
HLE
4.6%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.116
SciCode
11.7%
Math

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

AIME
0.7%
Math 500
40.3%

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.