AI model comparison

GPT-4.1 vs Gemini 2.5 Pro

Comparing two large-context flagships for long-document processing, retrieval-augmented generation, and code-heavy development work.

Quick answer: Both list 1M-token context windows, but pricing splits by direction: Gemini 2.5 Pro lists the cheaper input ($1.25 vs $2 per 1M) while GPT-4.1 lists the cheaper output ($8 vs $10 per 1M), so which is cheaper depends on your input/output mix.

Pricing & context

All figures below are list prices pulled directly from the Harpd pricing registry (last verified 2026-08-18). Prices change often — open each model’s source link to confirm before budgeting. Machine-readable copy: /data/llm-pricing.json.

MetricGPT-4.1Gemini 2.5 Pro
ProviderOpenAIGoogle
Input / 1M tokens$2$1
Output / 1M tokens$8$10
Cached input / 1M tokens$0.2$0.13
Batch discount50% off50% off
Context window1,000,000 tokens1,000,000 tokens
SourceOpenAI pricing ↗Google pricing ↗
Gemini 2.5 Pro lists the lower cost for this workload.

$1,625 vs $1,800 / month (100k calls, 5k in / 1k out)

Last updated
Methodology
Computed from list prices per 1M tokens; excludes prompt caching and batch discounts.
Harpd has not measured these two models head-to-head yet.

First benchmark: JSON extraction, 100 real tasks — target ship 2026-09-15

Last updated
Methodology
Until then, no performance win is claimed on this page — validate both models on your own tasks.
GPT-4.1

Capabilities

GPT-4.1 pairs a 1M token context window with strong code generation and instruction-following, plus prompt caching to reduce repeat-prefix cost.

Best for

Generation-heavy long-context work — the $8 per 1M output price wins whenever outputs dominate the token mix — and code-centric workflows.

Limitations

At $2 per 1M input it lists above Gemini 2.5 Pro for read-heavy workloads, and there is no documented multimodal parity claim in this comparison.

Gemini 2.5 Pro

Capabilities

Gemini 2.5 Pro also provides a 1M token context window, adds native multimodal input and strong reasoning, and includes prompt caching for long-context use. Prompts over 200k tokens bill at higher rates ($2.50 input / $15 output per 1M).

Best for

Input-heavy long-context work, multimodal documents, and retrieval where most tokens are read rather than written.

Limitations

Output at $10 per 1M is the pricier direction of this pair, and the >200k-token tier ($2.50/$15) removes the input-price advantage on very large prompts.

Recommendation

Both models sit at the top of the capability range and share a 1M-token context, so either can handle very long inputs. The better fit depends on whether your workload is code-centric, multimodal, or reasoning-heavy — and on which direction your token mix flows. Price alone will not tell you which is safe to adopt — measure both on your real tasks.

Updated 2026-08-18. Sources: OpenAI and Google official pricing pages (verified 2026-08-18); no Harpd-measured benchmark yet.

Methodology & sources

Prices on this page come from the Harpd pricing registry, which mirrors the officialOpenAI and Google pricing pages and was last verified 2026-08-18. Capability notes summarize documented provider positioning — they are not Harpd measurements. Cheaper-cost claims are computed from the registry at a fixed reference workload, so they are reproducible from the published dataset. Read the pricing methodology and themodel replacement guide before switching a production workload.

Answer

Price tells you what a model costs. It does not tell you whether it can replace your current model on your real tasks.

Read the evaluation guide
Evidence

Related comparisons

Claude Sonnet 4.5 vs GPT-4.1GPT-4.1 lists lower prices than Claude Sonnet 4.5 ($2 vs $3 per 1M input, $8 vs $15 per 1M output) and a 1M-token context window versus 200k, while Claude Sonnet 4.5 is positioned around agentic reliability, tool calling and structured output.Claude Sonnet 4.5 vs Gemini 2.5 ProGemini 2.5 Pro lists a lower input price ($1.25 vs $3 per 1M) and a 1M-token context window versus 200k; Claude Sonnet 4.5 lists a higher output price ($15 vs $10 per 1M) and is positioned around agentic reliability and structured output.

Explore related tools

Model Compare

Side-by-side pricing and capability explorer for every model in the registry.

LLM Pricing

Full list-price table across providers, updated from official sources.

Benchmarks

Open benchmark data — methodology, datasets and published results.

GPT-4.1 vs Gemini 2.5 Pro, answered

Which is cheaper, GPT-4.1 or Gemini 2.5 Pro?
Gemini 2.5 Pro lists the lower cost at Harpd's reference workload (100,000 calls/month, 5,000 input / 1,000 output tokens): about $1,625 versus $1,800 per month at list prices (1.25/10 vs 2/8 USD per 1M input/output tokens). The mix matters: if your workload is output-heavy, recompute with the model compare calculator.
What is the main difference between GPT-4.1 and Gemini 2.5 Pro?
Both list 1M-token context windows, but pricing splits by direction: Gemini 2.5 Pro lists the cheaper input ($1.25 vs $2 per 1M) while GPT-4.1 lists the cheaper output ($8 vs $10 per 1M), so which is cheaper depends on your input/output mix.
Which is cheaper for a typical workload?
It depends on the token mix. At Harpd's reference workload (100k calls/month, 5,000 input / 1,000 output tokens), Gemini 2.5 Pro comes out cheaper because input dominates: roughly $1,625/month versus $1,800/month at list prices. Flip the mix to output-heavy — say 1,000 in / 5,000 out — and GPT-4.1 wins (~$4,200 vs ~$5,125). The model compare calculator recomputes this for your actual numbers.
Which should I choose?
Both models sit at the top of the capability range and share a 1M-token context, so either can handle very long inputs. The better fit depends on whether your workload is code-centric, multimodal, or reasoning-heavy — and on which direction your token mix flows. Price alone will not tell you which is safe to adopt — measure both on your real tasks.