Ollama vs Gemini vs Claude: 90-Day Cost Test
Table of Contents
- 1. What Do You Actually Get for Free?
- 2. Hidden Costs: Every Trap We Actually Hit
- Gemini's Trap: "Free" Can Become $128 Overnight
- Claude's Trap: The Dynamic Usage Cap Black Box
- Ollama's Trap: The Electricity Bill Nobody Talks About
- 3. Real-World Cost: How Much Per 1,000 Requests?
- Assumptions
- Cost Comparison Table
- 4. When to Use What: The Decision Tree
- The Cheat Sheet
- 5. The Combo Strategy: Using All Three Is the Real Answer
- Monthly Cost Breakdown
- 6. What We'd Tell You After 90 Days
- Gemini Free Tier
- Claude Pro
- Ollama Local Inference
- The Actual Answer
- Want to See How AI Understands Your Website?
"Saving money isn't about picking the cheapest tool. It's about making every dollar hit the right model."
Short answer, Ollama vs Gemini (and Claude): Gemini's free tier was our pick for fast, high-volume work and long documents (1M-token context). Ollama won for unlimited, offline, private batch jobs, but ran at 13.2 tok/s on our RTX 3060 Ti. Claude Pro handled anything we couldn't afford to get wrong. Using all three cost us about $30.50/month for 160,000+ requests.
For the first three months of 2026, Ultra Lab ran three LLM stacks in parallel production:
- Google Gemini 2.5 Flash (free tier, capped at 1,500 requests/day): handling part of the work for 4 AI agents
- Claude Opus 4.6 (Pro plan, $20/mo): handling all core development, code review, and writing
- Ollama + ultralab:7b (RTX 3060 Ti local inference): running content generation and batch jobs
After 90 days of parallel operation, we have real cost-performance data: not whitepaper numbers, but figures pulled from our billing dashboards and production logs every single day.
This post lays it all out.
1. What Do You Actually Get for Free?
The spec-sheet comparison:
| Metric | Gemini 2.5 Flash (Free) | Claude Pro ($20/mo) | Ollama ultralab:7b (Local) |
|---|---|---|---|
| Monthly cost | $0 | $20 | $0 (software is free) |
| Daily request limit | 1,500 RPD | Dynamic (usage-based) | Unlimited |
| Model class | Flash (fast, shallow) | Opus 4.6 (top-tier reasoning) | 7B params (lightweight) |
| Context window | 1M tokens | 200K tokens (1M available) | 16,384 tokens |
| Inference speed | ~80 tok/s | ~40 tok/s | 13.2 tok/s |
| Code ability | ★★★☆ | ★★★★★ | ★★☆☆☆ |
| Offline capable | No | No | Yes |
Looks like each has its strengths. But spec sheets and production reality are very different things.
2. Hidden Costs: Every Trap We Actually Hit
Gemini's Trap: "Free" Can Become $128 Overnight
The biggest problem with Gemini's free tier isn't the quota. It's the billing landmine.
On March 7, 2026, one of our Gemini API keys was attached to a billing-enabled GCP project. When the free quota ran out that day, the system didn't warn us. It silently switched to pay-per-use billing. We woke up to a $127.80 charge on a single overnight run.
Lessons learned the hard way:
⚠️ NEVER create API keys from billing-enabled GCP projects
⚠️ Always create keys under a project with billing DISABLED
⚠️ Set reasoning parameter to false (otherwise token consumption spikes 3-5x per request)
That reasoning: true flag deserves special mention. With it enabled, every single request consumed 3-5x more tokens for the "thinking" process. After we set it to false, token usage for identical tasks dropped 70%. On a 1,500 RPD free quota, that effectively tripled our usable throughput.
Claude's Trap: The Dynamic Usage Cap Black Box
Claude Pro pricing looks simple: $20/month, use as much as you want. In practice:
- Usage caps adjust dynamically based on overall demand; you get throttled during peak hours
- Opus 4.6 model consumes 5x the quota of Sonnet
- There's no official token usage dashboard, so you genuinely don't know how much you have left
The silver lining: Taiwan daytime is US off-peak. During Anthropic's limited-time promotion from March 13 to 27, 2026, usage caps doubled outside the weekday peak of 8 AM to 2 PM ET (8 PM to 2 AM Taiwan time). During the promotion we scheduled all heavy tasks (long-form docs, full code reviews, architecture decisions) during Taiwan business hours, effectively getting $40 of value for $20.
Ollama's Trap: The Electricity Bill Nobody Talks About
Local inference means zero API fees. But GPUs don't run on enthusiasm.
Our measured data (RTX 3060 Ti, 8GB VRAM):
| Metric | Value |
|---|---|
| GPU power draw during inference | ~180W |
| Idle power draw | ~15W |
| Daily inference time | ~6 hours |
| Monthly electricity (Taiwan rate ~$0.11/kWh) | ~$10.50 |
| Model cold start time | 2-3 seconds |
| Real feel at 13.2 tok/s | Usable but noticeably slow |
There's another hidden cost: GPU contention. We once downloaded a new model while Ollama was running inference. Speed dropped from 13.2 tok/s to 0.1 tok/s, effectively unusable. If your GPU is shared with gaming, rendering, or training, your "free" inference has an opportunity cost.
3. Real-World Cost: How Much Per 1,000 Requests?
We compiled three months of production data into unit economics:
Assumptions
- Average request: 800 input tokens + 400 output tokens
- 500 effective requests per day (excluding failures and retries)
- Monthly total cost calculation
Cost Comparison Table
| Metric | Gemini Free | Claude Pro | Ollama Local |
|---|---|---|---|
| Monthly cost | $0 | $20 | $10.50 (electricity) |
| Monthly available requests | ~45,000 | ~15,000* (dynamic) | Unlimited |
| Cost per 1K requests | $0 | ~$1.33 | ~$0.10** |
| Quality score (our subjective rating) | 72/100 | 95/100 | 58/100 |
| Cost per quality point | $0 | $0.014/pt | $0.002/pt |
| Failure rate (quota/errors) | 3.2% | 1.1% | 0.4% |
*Claude caps vary by model and time of day; this is our Opus 4.6 estimate **Based on ~100K monthly inferences, electricity $10.50 / 100K
Note: Gemini's "$0 per 1K requests" assumes you haven't hit the billing landmine. If you do, your single-month cost can spike 10x or more.
4. When to Use What: The Decision Tree
After three months of production use, here's the decision logic we settled on:
What kind of task are you running?
│
├─ Requires top-tier reasoning (code, architecture, complex writing)
│ └─→ Claude Opus 4.6
│ Schedule during Taiwan daytime (US off-peak)
│
├─ High-volume repetitive tasks (social posts, replies, tagging)
│ └─→ Gemini 2.5 Flash (Free)
│ Set reasoning: false
│ Bind API key to billing-disabled project
│
├─ Needs offline / privacy / unlimited quota
│ └─→ Ollama local inference
│ Best for: content drafts, data cleaning, batch processing
│
├─ Long context (>100K tokens)
│ └─→ Gemini (1M context window)
│ Claude works too but eats more quota
│
└─ Low latency required (<2 second response)
└─→ Gemini Flash > Claude Sonnet > Ollama
Local inference at 13.2 tok/s is too slow for real-time
The Cheat Sheet
| Use Case | Best Choice | Why |
|---|---|---|
| Writing code / architecture | Claude | Quality gap is too large |
| Social media agent automation | Gemini Free | 1,500 RPD free, volume matters |
| Batch content generation | Ollama | Unlimited quota, latency doesn't matter |
| Long document analysis | Gemini | 1M context, nothing else comes close |
| Customer-facing real-time responses | Gemini Flash | Fast and free |
| Sensitive data processing | Ollama | Data never leaves your machine |
5. The Combo Strategy: Using All Three Is the Real Answer
Here's what our production architecture looked like as of 2026-04-02:
┌─────────────────────────────────────────────────┐
│ Ultra Lab LLM Architecture │
├─────────────────────────────────────────────────┤
│ │
│ ┌──────────┐ High-quality ┌──────────────┐ │
│ │ │ ───────────────→ │ Claude Opus │ │
│ │ │ (dev/writing) │ $20/mo │ │
│ │ │ └──────────────┘ │
│ │ │ │
│ │ Task │ High-volume ┌──────────────┐ │
│ │ Router │ ───────────────→ │ Gemini Flash │ │
│ │ │ (Agent Fleet) │ $0/mo │ │
│ │ │ └──────────────┘ │
│ │ │ │
│ │ │ Batch/offline ┌──────────────┐ │
│ │ │ ───────────────→ │ Ollama 7B │ │
│ └──────────┘ (content gen) │ $10.50/mo │ │
│ └──────────────┘ │
├─────────────────────────────────────────────────┤
│ Monthly total: ~$30 │
│ Monthly capacity: 160,000+ effective requests │
│ Equivalent Claude-only API cost: $600+ │
└─────────────────────────────────────────────────┘
Monthly Cost Breakdown
| Component | Cost | Share | Requests |
|---|---|---|---|
| Claude Pro | $20.00 | 66% | ~15,000 |
| Gemini Free | $0.00 | 0% | ~45,000 |
| Ollama electricity | $10.50 | 34% | ~100,000+ |
| Monthly total | $30.50 | 100% | 160,000+ |
If we ran the same volume entirely through Claude API (not Pro subscription, but pay-per-token), the monthly cost would be roughly $600-800.
Our combo strategy costs 4-5% of a cloud-only approach.
6. What We'd Tell You After 90 Days
Gemini Free Tier
Use it for: High-volume, medium-quality automation tasks Don't use it for: Anything requiring precise reasoning Survival rule: You must be 100% certain your API key isn't attached to a billing-enabled project
Claude Pro
Use it for: Core development, high-quality content, anything you can't afford to get wrong Don't use it for: High-volume repetitive batch work (burns through quota fast) Bonus (only during Anthropic's March 13 to 27, 2026 promotion, now ended): If you were in Asia, your timezone naturally gave you off-peak dividends
Ollama Local Inference
Use it for: Batch content generation, data cleaning, offline scenarios, privacy-sensitive workloads Don't use it for: Real-time responses, complex reasoning, or when your GPU is already busy Prerequisite: A decent discrete GPU (8GB+ VRAM minimum)
The Actual Answer
There is no "cheapest single option." There is only the cheapest combination.
If you can only pick one:
- $0 budget → Gemini Free (watch out for billing landmines)
- $20 budget → Claude Pro (quality is irreplaceable)
- Already own a GPU → Ollama (marginal cost approaches zero)
Don't own a GPU yet? Our local GPU vs cloud API break-even analysis amortizes a $300 used RTX 3060 Ti over 36 months and shows whether, and when, it pays for itself against budget, mid-range and frontier APIs.
If you use all three:
- $30/month for the throughput capacity of $600+ in pure cloud API costs.
That's not optimization. That's arbitrage.
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Data in this post is based on Ultra Lab's actual production records from Q1 2026 (January-March). Hardware: RTX 3060 Ti / 32GB RAM / Windows 11 + WSL2. All USD figures based on approximate exchange rates at time of writing.
FAQ
Is Ollama or Gemini cheaper?
In our 90-day test, Gemini's free tier cost $0 per 1,000 requests, as long as the API key was not tied to a billing-enabled project. Ollama running a 7B model on an RTX 3060 Ti cost about $0.10 per 1,000 requests in electricity, roughly $10.50 a month.
Is Ollama faster than Gemini?
Not on our hardware. Gemini 2.5 Flash ran at about 80 tokens per second, while our Ollama 7B model on an RTX 3060 Ti ran at 13.2 tokens per second: usable for batch jobs, too slow for real-time responses.
When should you use Ollama instead of Gemini?
Use Ollama for batch content generation, data cleaning, offline work and sensitive data that should never leave your machine, since it has no request limit. Use Gemini's free tier for high-volume automation, fast responses and long documents that need its 1M-token context window.
Can the Gemini API free tier charge you?
Yes. If the API key is attached to a billing-enabled GCP project, the system gives no warning when the free quota runs out and silently switches to pay-per-use billing. That is how we woke up to a $127.80 charge after a single night. Always create keys under a project with billing disabled.