Cost and performance comparison
Gemini 3.1 Pro vs GPT-6.1 Sol
Model AGoogle
Gemini 3.1 Pro
- Cost / 1M Tokens
- $5.00
- 70% in · 30% out mix
- Value Index (score÷cost)
- 18.4
- Higher = better value
- Speed
- 82/100
- Context
- 1.0M
- Tier
- Smart Tier
List price from the Google pricing page, verified October 12, 2026.
Check the sourceModel BOpenAI
GPT-6.1 Sol
- Cost / 1M Tokens
- $4.40
- 70% in · 30% out mix
- Value Index (score÷cost)
- 22.0
- Higher = better value
- Speed
- 85/100
- Context
- 1.1M
- Tier
- Smart Tier
List price from the OpenAI pricing page, verified October 12, 2026.
Check the sourceIn-depth analysis
Gemini 3.1 Pro vs GPT-6.1 Sol: Detailed Comparison
Gemini 3.1 Pro is Google's mid-range-tier language model with a 1.0M-token context window, excelling at vision/multimodal. GPT-6.1 Sol from OpenAI is a mid-range-tier model supporting 1.1M tokens in context, with standout performance in reasoning.
Price is not the deciding factor here. For the same text, GPT-6.1 Sol and Gemini 3.1 Pro land within 9% of each other, which is inside the margin your own input/output ratio will move anyway. Pick on capability, context, or latency instead — the monthly bill will look much the same either way. Gemini 3.1 Pro is priced at $2.00/M input tokens and $12.00/M output tokens. GPT-6.1 Sol costs $2.00/M input and $10.00/M output.
On iOPTERA's editorial scores, GPT-6.1 Sol leads with 96/100 for coding and 97/100 for reasoning, against Gemini 3.1 Pro's 88/100 and 89/100.
Capability breakdown
Across the five core benchmark categories, here is how Gemini 3.1 Pro and GPT-6.1 Sol stack up head to head:
Best model by task
- coding: GPT-6.1 Sol wins with 96/100
- reasoning: GPT-6.1 Sol wins with 97/100
- data extraction: GPT-6.1 Sol wins with 95/100
- creative tasks: GPT-6.1 Sol wins with 94/100
Estimated monthly cost at scale
For 10M + 2M tokens of text a month (counted the way OpenAI's tokenizer counts), Gemini 3.1 Pro runs about $42.68 while GPT-6.1 Sol runs about $40.00 — GPT-6.1 Sol saves roughly $2.68 (6%) every month.
What actually decides it
Gemini 3.1 Pro and GPT-6.1 Sol come from different labs, which means different tokenizers, different API shapes, and a second vendor relationship. The same English text does not produce the same token count on both, so a price-per-million comparison understates the difference — measure your own prompts on each before treating the headline rates as the full story.
GPT-6.1 Sol and Gemini 3.1 Pro were released within 7 months of each other, so they are competing on the same evaluations under roughly the same conditions. That makes a direct benchmark comparison meaningful here in a way it usually is not — neither model has the advantage of being measured on a newer, easier set of tests.
Context is effectively a tie: 1.1M against 1.0M. Neither model unlocks a document size the other cannot handle, so this axis should not enter the decision. If you were hoping context would break the tie for you, it will not.
Throughput is close enough to ignore — 85/100 versus 82/100. Neither model will feel noticeably quicker in an interactive product, so latency is not a reason to choose between them.
With prices this close (9% apart), pick on fit rather than cost. GPT-6.1 Sol holds the benchmark edge; weigh that against where each one is weakest — coding and creative tasks respectively — and use the calculator above with your own token mix.
What you actually pay
The list price is only the start. Prompt caching, batch processing and long prompts change the bill, and each provider sets them differently. Prices per 1M tokens.
| Pricing term | Gemini 3.1 Pro | GPT-6.1 Sol |
|---|---|---|
| List price (input / output) | $2.00 / $12.00 | $2.00 / $10.00 |
| Cached input | $0.2010% of the input price | $0.105% of the input price |
| Batch API | 50% off input and output | 50% off input and output |
| Long prompts | Over 200K tokens: $4.00 / $18.00 | Over 272K tokens: $4.00 / $15.00 |
| Input with 80% cache hits | $0.56 | $0.48 |
With 80% of input served from the cache, GPT-6.1 Sol costs 12% less than Gemini 3.1 Pro for the same input text, counting each model's tokenizer.The tokenizer factor for Gemini 3.1 Pro is our estimate until it is measured.
The last row assumes 80% of input tokens are read from the cache, as in a chat app that re-sends a long system prompt or RAG over the same documents. Cache-write and storage charges are not included.
Gemini also bills cache storage by the hour.
Checked on October 8, 2026 against the pricing pages of Google, OpenAI.
Capability scores
iOPTERA editorial scores across 5 categories — informed by public benchmarks, not measured by us
Coding
Reasoning
Extraction
Creative
Vision
Speed Score
Context Window
What Is a Token?
Models don't read words — they process tokens.
A token is roughly 4 characters of English text (~¾ of a word). Your API bill is priced per million tokens — understanding this directly reduces your costs.
Short phrase
"Hello, world!"
- Gemini 3.1 Pro
- $0.78
- GPT-6.1 Sol
- $0.80
Business email
One typical email (~200 words)
- Gemini 3.1 Pro
- $52.38
- GPT-6.1 Sol
- $54.00
Code file
50-line Python script
- Gemini 3.1 Pro
- $77.60
- GPT-6.1 Sol
- $80.00
Prices shown are for 100,000 runs of each workload — one run costs a fraction of a cent on both Gemini 3.1 Pro and GPT-6.1 Sol, so the number only becomes meaningful at production volume. Input tokens only; add your output volume in the calculator below.
How to check your token usage
response.usage.total_tokensEvery API response includes a usage object. Sum total_tokens across all calls to get your monthly figure, then use the calculator below.
Your Cost Calculator
Enter your actual monthly token usage to see real savings
Quick Presets
Gemini 3.1 Pro
$145.50/mo
$1,746.00/yr
GPT-6.1 Sol
$132.00/mo
$1,584.00/yr
Annual Savings
$162.00 saved per year
GPT-6.1 Sol cheaper · $13.50/mo
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