Gemini 1.5 Pro vs Gemini 2.5 Pro preview 06 05
Gemini 1.5 Pro (Google Vertex AI, 2,097,152-token context) versus Gemini 2.5 Pro preview 06 05 (Google Vertex AI, 1,048,576-token context). Gemini 1.5 Pro is cheaper by 44% on a blended token mix. Gemini 2.5 Pro preview 06 05 uniquely supports prompt caching and native reasoning mode. Use the live calculator below to plug your real usage shape into both, then route the winner via Agent Command Center for shadow A/B without code changes.
Bottom line — Gemini 1.5 Pro vs Gemini 2.5 Pro preview 06 05
Gemini 1.5 Pro and Gemini 2.5 Pro preview 06 05 target overlapping workloads but differ sharply on economics. Gemini 1.5 Pro runs roughly 44% cheaper on a blended input-plus-output token mix, which translates to approximately $3,000 per month at mid-market volume (100K requests/day). The gap compounds at enterprise scale, making the cost axis the first filter most teams apply when deciding between these two models.
Gemini 1.5 Pro ships a 2,097,152-token context window, 2.0x larger than Gemini 2.5 Pro preview 06 05's 1,048,576 tokens. That headroom matters for long-document RAG pipelines, multi-turn agent sessions that accumulate tool-call history, and codebases where the entire repository needs to fit in a single prompt. If your average prompt stays under 1,048,576 tokens, the extra context on Gemini 1.5 Pro is insurance you may never use — and Gemini 2.5 Pro preview 06 05 may win on other axes.
On capability surface area, the models diverge: Gemini 2.5 Pro preview 06 05 supports prompt caching where the other does not; Gemini 2.5 Pro preview 06 05 supports native reasoning mode where the other does not. These differences are binary — either your workload needs the capability or it does not. Check whether any critical path in your agent pipeline depends on a capability only one model provides before committing to a migration.
For teams evaluating both models, the recommended path is a shadow A/B test: route production traffic through an OpenAI-compatible gateway, mirror a percentage to the candidate model, score both responses with an automated evaluator (faithfulness, tool-call correctness, latency), and compare cohort-level metrics over two weeks. Future AGI Agent Command Center supports this pattern with a single `base_url` change and built-in evaluators from the ai-evaluation SDK.
Live workload comparison
Same workload run through both models. The cheaper one is highlighted.
strategy: cost-optimized
primary:
model: gemini-1-5-pro
provider: vertex-ai
fallback:
model: gemini-2-5-pro-preview-06-05
provider: vertex-ai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Gemini 1.5 Pro | Gemini 2.5 Pro preview 06 05 | |
|---|---|---|
| Input price | $1.25/M | $1.25/M |
| Output price | $5.00/M | $10.00/M |
| Context window | 2,097,152 | 1,048,576 |
| Max output | 8,192 | 65,535 |
| Function calling | ✓ | ✓ |
| Vision | ✓ | ✓ |
| Audio input | — | — |
| Reasoning | — | ✓ |
| Prompt caching | — | ✓ |
| Structured output | ✓ | ✓ |
| Pricing verified | May 7, 2026 | May 7, 2026 |
Benchmark comparison
Side-by-side public benchmark scores. Greener bar = winner.
Cost at scale: monthly spend at three usage volumes
Estimated monthly cost assuming 1,000 input + 200 output tokens per request — a realistic chat-agent shape. Adjust your own usage in the calculator at the top of this page for an exact number.
| Scale | Gemini 1.5 Pro | Gemini 2.5 Pro preview 06 05 | Delta |
|---|---|---|---|
| Startup 10K requests/day | $675 /mo | $975 /mo | $300/mo |
| Mid-market 100K requests/day | $6,750 /mo | $9,750 /mo | $3,000/mo |
| Enterprise 1M requests/day | $67,500 /mo | $97,500 /mo | $30,000/mo |
At enterprise scale (1M requests/day), a difference of even ~10% in unit price compounds into thousands of dollars per month. Cached input pricing and batch tiers can shift this further — both are surfaced on each model's own page.
When to choose which
Picked from the data above — not vendor marketing. Match the rules to your workload, not the other way around.
You're cost-sensitive at scale — Gemini 1.5 Pro runs ~44% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
Your workload needs long context — Gemini 1.5 Pro fits 2,097,152 tokens versus the other model's 1,048,576, enough headroom for full books, large codebases, or 100+ page documents in one shot.
Your tasks involve multi-step planning or math-heavy reasoning — Gemini 2.5 Pro preview 06 05 ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
You re-send the same large system prompt across requests — Gemini 2.5 Pro preview 06 05 supports prompt caching, cutting input cost on repeat hits.
Capability diff — what you gain and lose on the swap
A specific list of what each model has that the other doesn't. If your workload depends on a row in Only Gemini 1.5 Pro, switching to Gemini 2.5 Pro preview 06 05 means re-architecting that path (and vice versa).
- • Prompt caching
- • Native reasoning mode
Capabilities both share (6)
- ✓ Function calling
- ✓ Parallel tool calls
- ✓ Vision input
- ✓ PDF input
- ✓ Streaming
- ✓ Structured output (JSON schema)
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes down 50% when moving from Gemini 1.5 Pro (2,097,152) to Gemini 2.5 Pro preview 06 05 (1,048,576). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 8,192 on Gemini 1.5 Pro vs 65,535 on Gemini 2.5 Pro preview 06 05. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Gemini 2.5 Pro preview 06 05 has capabilities Gemini 1.5 Pro lacks: Prompt caching, Native reasoning mode. Worth wiring through the agent design before commit.
- Pricing on Gemini 2.5 Pro preview 06 05 was last verified 180 days ago — confirm against the provider's published rate card before committing to a multi-month migration.
How to A/B test Gemini 1.5 Pro vs Gemini 2.5 Pro preview 06 05 in production
If you're stuck between the two, run them side-by-side on real traffic. Four steps the Future AGI team uses internally:
- 1. Point your existing OpenAI SDK at
https://gateway.futureagi.com/v1. No code change beyondbase_urland a virtual key. - 2. Mark Gemini 1.5 Pro primary, mirror 20% of traffic to Gemini 2.5 Pro preview 06 05 in shadow mode. Both responses are logged; only the primary is served to users.
- 3. Score every shadow response with an evaluator — faithfulness, tool-call correctness, response latency, cost. Built-in evaluators in ai-evaluation cover the common axes.
- 4. Compare cohort-level metrics after two weeks. Switch primary when the candidate wins on what matters to your workload — and stays within your latency budget.
Full walkthrough on the Agent Command Center page.
FAQ — Gemini 1.5 Pro vs Gemini 2.5 Pro preview 06 05
Which is cheaper, Gemini 1.5 Pro or Gemini 2.5 Pro preview 06 05? ▾
Gemini 1.5 Pro is cheaper by roughly 44% on a blended input + output token mix. Input prices are $1.25/M for Gemini 1.5 Pro versus $1.25/M for Gemini 2.5 Pro preview 06 05; output prices are $5.00/M versus $10.00/M. The exact savings depend on your input:output ratio — use the live calculator above to plug in your own request shape.
What is the context window of Gemini 1.5 Pro versus Gemini 2.5 Pro preview 06 05? ▾
Gemini 1.5 Pro supports up to 2,097,152 tokens of context. Gemini 2.5 Pro preview 06 05 supports up to 1,048,576 tokens. Gemini 1.5 Pro has the larger window by a factor of 2.0x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Gemini 1.5 Pro and Gemini 2.5 Pro preview 06 05 both support tool calling? ▾
Yes — both Gemini 1.5 Pro and Gemini 2.5 Pro preview 06 05 support native function calling. Both also support structured output via JSON schema, so an agent can be ported between them with the same tool definitions.
Which model supports prompt caching for cost reduction? ▾
Gemini 2.5 Pro preview 06 05 supports prompt caching; the other does not. If your agent has a stable system prompt + retrieval context block that repeats across requests, Gemini 2.5 Pro preview 06 05 gives you a 50–90% discount on those repeated input tokens at the provider level.
When should I choose Gemini 1.5 Pro over Gemini 2.5 Pro preview 06 05? ▾
You're cost-sensitive at scale — Gemini 1.5 Pro runs ~44% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume. Your workload needs long context — Gemini 1.5 Pro fits 2,097,152 tokens versus the other model's 1,048,576, enough headroom for full books, large codebases, or 100+ page documents in one shot.
When should I choose Gemini 2.5 Pro preview 06 05 over Gemini 1.5 Pro? ▾
Your tasks involve multi-step planning or math-heavy reasoning — Gemini 2.5 Pro preview 06 05 ships a native reasoning mode that explicitly thinks before responding, the other doesn't. You re-send the same large system prompt across requests — Gemini 2.5 Pro preview 06 05 supports prompt caching, cutting input cost on repeat hits.
How do I A/B test Gemini 1.5 Pro against Gemini 2.5 Pro preview 06 05 in production? ▾
Route both through an OpenAI-compatible gateway like Future AGI Agent Command Center with shadow mode enabled. Send 100% of traffic to your primary model, mirror 10–20% to the candidate, score every response with an evaluator (faithfulness, tool-call correctness, response time), and compare cohort-level metrics for two weeks. Switch when the candidate wins on the metrics that matter to your workload and stays within your latency budget.