LLM Reference

Gemini 2.5 Pro vs Gemini 3.5 Flash

Choose Gemini 2.5 Pro when lower standard input cost matters more; choose Gemini 3.5 Flash when lower output cost is the better fit. Both expose about 1M tokens of context. No general Coding winner is shown because Google's published results use different SWE-bench variants, model releases, and agent configurations.

Choose Gemini 2.5 Pro when its lower standard input price or established Pro behavior fits the workload; choose Gemini 3.5 Flash when its lower output price, newer Flash surface, or latency profile is the better fit. Treat coding performance as an evaluation gap until both exact variants are published on the same benchmark version and compatible harness.

Decision scorecard

Local evidence first
SignalGemini 2.5 ProGemini 3.5 Flash
Best forreasoning-heavy apps, multimodal apps, and tool-calling agentsreasoning-heavy apps, multimodal apps, and tool-calling agents
Decision fitCoding, RAG, and AgentsCoding, RAG, and Agents
Context window1m1.05m
Cheapest output$10/1M tokens$9/1M tokens
Provider routes4 tracked4 tracked
Shared benchmarksWithheldWithheld

Decision tradeoffs

Choose Gemini 2.5 Pro when...
  • Local decision data tags Gemini 2.5 Pro for Coding, RAG, and Agents.
Choose Gemini 3.5 Flash when...
  • Gemini 3.5 Flash has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • Gemini 3.5 Flash has the lower cheapest tracked output price at $9/1M tokens.
  • Local decision data tags Gemini 3.5 Flash for Coding, RAG, and Agents.

Monthly cost at traffic

Estimate token spend from the cheapest tracked input and output route or tier on this page.

Lower estimate Gemini 3.5 Flash

Gemini 2.5 Pro

$3,500

Cheapest tracked route/tier: Google AI Studio <=200K tokens

Gemini 3.5 Flash

$3,450

Cheapest tracked route/tier: Google AI Studio

Estimated monthly gap: $50.00. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.

Switch friction

Gemini 2.5 Pro -> Gemini 3.5 Flash
  • Provider overlap exists on Google AI Studio, GCP Vertex AI, and Vercel AI Gateway; start route-level A/B tests there.
  • Gemini 3.5 Flash is $1/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
Gemini 3.5 Flash -> Gemini 2.5 Pro
  • Provider overlap exists on Google AI Studio, GCP Vertex AI, and OpenRouter; start route-level A/B tests there.
  • Gemini 2.5 Pro is $1/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.

Specs

Specification
Released2025-06-172026-05-19
Context window1m1.05m
Parameters
ArchitectureDecoder OnlyDecoder Only
LicenseProprietaryProprietary
OpennessProprietaryProprietary
WeightsNot releasedNot released
CodeUnknownUnknown
Commercial useCommercial use: conditionalCommercial use: conditional
Knowledge cutoff2025-012025-01

Pricing and availability

Pricing attributeGemini 2.5 ProGemini 3.5 Flash
Input price
<=200K tokens
$1.25/1M tokens
Standard Gemini 2.5 Pro pricing for prompts up to 200K tokens.
>200K tokens
$2.50/1M tokens
Higher Gemini 2.5 Pro tier for prompts above 200K tokens.
$1.50/1M tokens
Output price
<=200K tokens
$10/1M tokens
Standard Gemini 2.5 Pro pricing for prompts up to 200K tokens.
>200K tokens
$15/1M tokens
Higher Gemini 2.5 Pro tier for prompts above 200K tokens.
$9/1M tokens
Providers

Capabilities

CapabilityGemini 2.5 ProGemini 3.5 Flash
VisionYesYes
MultimodalYesYes
ReasoningYesYes
Function callingYesYes
Tool useYesYes
Structured outputsYesYes
Code executionYesYes
IDE integrationNoNo
Computer useNoNo
Parallel agentsNoNo

Benchmarks

No like-for-like benchmark rows are shown for this pair.

Harness caveat. Google reports Gemini 2.5 Pro Experimental on SWE-bench Verified with a custom agent setup and Gemini 3.5 Flash on SWE-bench Pro Public with an internal Antigravity harness. Different benchmark variants and configurations do not support a coding winner.

Deep dive

Context is effectively tied for planning purposes. Gemini 2.5 Pro is tracked at 1,048,576 tokens and Gemini 3.5 Flash at 1,048,576 tokens, so neither gets a generic long-context win. Confirm provider-specific limits, media constraints, and maximum output behavior for the route you intend to call.

Standard direct pricing creates a workload-dependent tradeoff. Google's pricing data lists Gemini 2.5 Pro at $1.25/M input and $10/M output for prompts up to 200K tokens, with a higher tier above 200K. Gemini 3.5 Flash is listed at $1.50/M input and $9/M output. Input-heavy workloads can favor 2.5 Pro; output-heavy workloads can favor 3.5 Flash.

The official coding rows are not like-for-like. Google's March 2025 2.5 Pro Experimental announcement reports 63.8% on SWE-bench Verified with a custom agent setup. The 3.5 Flash model card reports 55.1% on SWE-bench Pro Public, single attempt, using Google's internal Antigravity harness averaged over five runs. Different benchmark datasets and configurations cannot establish a head-to-head winner.

This page therefore suppresses the shared benchmark table for the pair instead of mixing similarly named rows from unrelated sources. Run both current model IDs through the same repository set, agent scaffold, tool permissions, attempt count, and evaluator before making a coding decision. Until then, price, context, provider availability, and your own latency and quality tests are the source-qualified signals.

FAQ

Which is cheaper, Gemini 2.5 Pro or Gemini 3.5 Flash?

It depends on token mix. Gemini 2.5 Pro starts at $1.25/M input and $10/M output for prompts up to 200K tokens; Gemini 3.5 Flash is $1.50/M input and $9/M output.

Which model has the larger context window?

Neither has a meaningful headline advantage in the tracked data. Both are listed at about 1,048,576 tokens, so verify route-specific media, output, and long-prompt constraints instead.

Which Gemini model is better for coding?

No source-qualified head-to-head winner is available. The published rows use SWE-bench Verified versus SWE-bench Pro Public, different model eras, and different agent configurations, so they should not be ranked directly.

What evidence would make the coding comparison valid?

Both exact model variants need results on the same benchmark version with compatible repository selection, agent harness, tools, sampling, attempt count, and evaluator. Until then, run a controlled internal evaluation.

Continue comparing

Last reviewed: 2026-06-29. Data sourced from public model cards and provider documentation.