Gemini 1.5 Pro vs GPT-4 Turbo
Gemini 1.5 Pro (Google Vertex AI, 2,097,152-token context) versus GPT-4 Turbo (Azure OpenAI, 128,000-token context). Gemini 1.5 Pro is cheaper by 86% on a blended token mix. Gemini 1.5 Pro uniquely supports vision input and pdf input. Across 3 public benchmarks we tracked, Gemini 1.5 Pro wins 3 and GPT-4 Turbo wins 0. 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 GPT-4 Turbo
Gemini 1.5 Pro and GPT-4 Turbo target overlapping workloads but differ sharply on economics. Gemini 1.5 Pro runs roughly 86% cheaper on a blended input-plus-output token mix, which translates to approximately $46,050 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, 16.4x larger than GPT-4 Turbo's 128,000 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 128,000 tokens, the extra context on Gemini 1.5 Pro is insurance you may never use — and GPT-4 Turbo may win on other axes.
On capability surface area, the models diverge: Gemini 1.5 Pro supports vision input where the other does not; Gemini 1.5 Pro supports pdf input where the other does not; Gemini 1.5 Pro supports structured output (json schema) 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.
Across 3 public benchmarks, Gemini 1.5 Pro leads on 3 and GPT-4 Turbo leads on 0. The widest gap is on arena-vision, where Gemini 1.5 Pro scores 67.0 points higher. Benchmarks are noisy and task-dependent — a model that leads on arena-elo may trail on code generation. The safest approach is to run both models on your own golden set before treating any benchmark as decisive.
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: gpt-4-turbo
provider: azure-openai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Gemini 1.5 Pro | GPT-4 Turbo | |
|---|---|---|
| Input price | $1.25/M | $11.00/M |
| Output price | $5.00/M | $33.00/M |
| Context window | 2,097,152 | 128,000 |
| Max output | 8,192 | 4,096 |
| Function calling | ✓ | ✓ |
| Vision | ✓ | — |
| Audio input | — | — |
| Reasoning | — | — |
| Prompt caching | — | — |
| Structured output | ✓ | — |
| Pricing verified | May 7, 2026 | Oct 22, 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 | GPT-4 Turbo | Delta |
|---|---|---|---|
| Startup 10K requests/day | $675 /mo | $5,280 /mo | $4,605/mo |
| Mid-market 100K requests/day | $6,750 /mo | $52,800 /mo | $46,050/mo |
| Enterprise 1M requests/day | $67,500 /mo | $528,000 /mo | $460,500/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 ~86% 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 128,000, enough headroom for full books, large codebases, or 100+ page documents in one shot.
Your inputs include screenshots, diagrams, or product photos — Gemini 1.5 Pro accepts image input natively, the other doesn't.
On arena-vision, Gemini 1.5 Pro scores 67.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
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 GPT-4 Turbo means re-architecting that path (and vice versa).
- • Vision input
- • PDF input
- • Structured output (JSON schema)
Capabilities both share (3)
- ✓ Function calling
- ✓ Parallel tool calls
- ✓ Streaming
Benchmark winners — by the numbers
For each public benchmark that has scores for both models, the higher score and the size of the gap. Benchmarks are noisy — treat anything under a 2-point delta as effectively tied.
| Benchmark | Gemini 1.5 Pro | GPT-4 Turbo | Winner | Δ |
|---|---|---|---|---|
| arena-elo | 1351.0 | 1325.0 | Gemini 1.5 Pro | +26.0 |
| arena-elo-coding | 1356.0 | 1347.0 | Gemini 1.5 Pro | +9.0 |
| arena-vision | 1179.0 | 1112.0 | Gemini 1.5 Pro | +67.0 |
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes down 94% when moving from Gemini 1.5 Pro (2,097,152) to GPT-4 Turbo (128,000). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 8,192 on Gemini 1.5 Pro vs 4,096 on GPT-4 Turbo. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Gemini 1.5 Pro has capabilities GPT-4 Turbo lacks: Vision input, PDF input, Structured output (JSON schema). Switching to GPT-4 Turbo means re-architecting any flow that depends on these.
- Provider changes from Google Vertex AI to Azure OpenAI. API authentication, rate-limit policy, regional availability, and billing all shift. Most teams route through an OpenAI-compatible gateway (e.g., Future AGI Agent Command Center) so the swap is a single `base_url` change instead of an SDK rewrite.
- Pricing on Gemini 1.5 Pro 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 GPT-4 Turbo 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 GPT-4 Turbo 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 GPT-4 Turbo
Which is cheaper, Gemini 1.5 Pro or GPT-4 Turbo? ▾
Gemini 1.5 Pro is cheaper by roughly 86% on a blended input + output token mix. Input prices are $1.25/M for Gemini 1.5 Pro versus $11.00/M for GPT-4 Turbo; output prices are $5.00/M versus $33.00/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 GPT-4 Turbo? ▾
Gemini 1.5 Pro supports up to 2,097,152 tokens of context. GPT-4 Turbo supports up to 128,000 tokens. Gemini 1.5 Pro has the larger window by a factor of 16.4x, 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 GPT-4 Turbo both support tool calling? ▾
Yes — both Gemini 1.5 Pro and GPT-4 Turbo 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.
Can Gemini 1.5 Pro and GPT-4 Turbo process images? ▾
Gemini 1.5 Pro accepts native image input. GPT-4 Turbo does not — you would need to route image-heavy workloads through Gemini 1.5 Pro or add a separate vision model in front of GPT-4 Turbo.
When should I choose Gemini 1.5 Pro over GPT-4 Turbo? ▾
You're cost-sensitive at scale — Gemini 1.5 Pro runs ~86% 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 128,000, enough headroom for full books, large codebases, or 100+ page documents in one shot. Your inputs include screenshots, diagrams, or product photos — Gemini 1.5 Pro accepts image input natively, the other doesn't. On arena-vision, Gemini 1.5 Pro scores 67.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
When should I choose GPT-4 Turbo over Gemini 1.5 Pro? ▾
On the data this page surfaces, GPT-4 Turbo is the right pick when Gemini 1.5 Pro's lower price or different capability profile aren't a fit for your workload. Run the live calculator above against your actual usage shape to confirm.
How do I A/B test Gemini 1.5 Pro against GPT-4 Turbo 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.