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Compare/Llama 3.2 Instruct 90B (Vision) vs Gemini 3.5 Flash (high)

Llama 3.2 Instruct 90B (Vision)vsGemini 3.5 Flash (high)

Side-by-side comparison of pricing, 12 benchmarks, and generation speed.

Meta

Llama 3.2 Instruct 90B (Vision)

Input
—
Output
—
Speed
—
TTFT
—
Google

Gemini 3.5 Flash (high)

Input
$1.5/M
Output
$9/M
Speed
216 tok/s
TTFT
18.36s

Winner by Category

Cheaper
Gemini 3.5 Flash (high)
Faster (tok/s)
Gemini 3.5 Flash (high)
Lower Latency
Gemini 3.5 Flash (high)
Benchmarks (0-2)
Gemini 3.5 Flash (high)

Pricing Comparison

MetricLlama 3.2 Instruct 90B (Vision)Gemini 3.5 Flash (high)
Input ($/M tokens)—$1.5
Output ($/M tokens)—$9
Cost for 1M input + 100K output tokens:
Gemini 3.5 Flash (high)$2.40

Speed Comparison

Output Speed (tokens/s) — higher is better
Llama 3.2 Instruct 90B (Vision)
—
Gemini 3.5 Flash (high)
216 tok/s
Time to First Token (seconds) — lower is better
Llama 3.2 Instruct 90B (Vision)
—
Gemini 3.5 Flash (high)
18.36s

Editorial Analysis

Verdict. Gemini 3.5 Flash (high) takes the aggregate benchmark matchup 2–0 across 2 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.

Pricing. Pricing varies significantly between these models — check the table above for the exact per-token rates. Many production workloads actually surface input-token cost (retrieval-augmented prompts, code-context windows), so factor both directions.

Strengths. Llama 3.2 Instruct 90B (Vision) is strongest on Intelligence Index (6.4). Gemini 3.5 Flash (high) leads on Coding Index (70.1), Intelligence Index (33.0).

Speed. Speed data is incomplete for this pair; benchmark and price should decide.

Provider. Meta and Google sell to overlapping but distinct developer audiences: Meta tends to ship frontier reasoning models with premium positioning, while Google often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.

Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
6.433.0
Coding Index
—70.1
Math Index
——
GPQA Diamond
——
MMLU-Pro
——
LiveCodeBench
——
AIME 2025
——
MATH-500
——
Humanity's Last Exam
——
SciCode
——
IFBench
——
TerminalBench
——
Llama 3.2 Instruct 90B (Vision)0 wins
2 winsGemini 3.5 Flash (high)

Frequently Asked Questions

Which is cheaper, Llama 3.2 Instruct 90B (Vision) or Gemini 3.5 Flash (high)?

Gemini 3.5 Flash (high) is cheaper overall. Its blended price (3:1 input/output ratio) is $3.38/M tokens vs $—/M for Llama 3.2 Instruct 90B (Vision).

Which model performs better on benchmarks?

Gemini 3.5 Flash (high) wins 2 out of 12 benchmarks compared to 0 for Llama 3.2 Instruct 90B (Vision). See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

Gemini 3.5 Flash (high) generates tokens faster at 216 tok/s vs — tok/s. However, Gemini 3.5 Flash (high) has lower time-to-first-token (18.36s vs —s).

When should I use Llama 3.2 Instruct 90B (Vision) vs Gemini 3.5 Flash (high)?

Choose based on your priorities: Gemini 3.5 Flash (high) for lower cost, Gemini 3.5 Flash (high) for stronger benchmark performance, and Gemini 3.5 Flash (high) for faster generation. For latency-sensitive apps, check the TTFT comparison above.