o3-mini
OpenAI · dense · 70B parameters · 200,000 context
Parameters
70B
Context Window
195K tokens
Architecture
Dense
Best GPU
B100 SXM
Cheapest API
$4.40/M
Quality Score
86/100
Intelligence Brief
o3-mini is a 70B parameter DENSE model from OpenAI, featuring Grouped Query Attention (GQA) with 64 layers and 8,192 hidden dimensions. With a 200,000 token context window, it supports tools, structured output, code, math, multilingual, reasoning. On standardized benchmarks, it achieves MMLU 87, HumanEval 80, GSM8K 96. The most cost-effective API deployment is via openai at $4.40/M output tokens. For self-hosted inference, B100 SXM delivers optimal throughput at $4271/month.
Provider pricing
2 providers · canonical: openai| Provider | Input $/M | Output $/M ▲ | Notes |
|---|---|---|---|
| openaicanonical | $1.10 | $4.40 | cheapest input · cheapest output |
| openrouter | $1.10 | $4.40 | cheapest input · cheapest output |
Prices update via the nightly pricing cron + admin approvals at /admin/ingest-queue. The leaderboard's Input/Output cells show the canonical rate above; this table shows the full spread.
Recent changes
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Picks: same family first, then same vendor within ±2× params, then top tag-overlap matches. Price shown is the cheapest Output $/M across providers — the row's page shows the canonical anchor.
Architecture Details
Memory Requirements
BF16 Weights
140.0 GB
FP8 Weights
70.0 GB
INT4 Weights
35.0 GB
GPU Compatibility Matrix
o3-mini is compatible with 38% of GPU configurations across 41 GPUs at 3 precision levels.
GPU Recommendations
BF16 · 1 GPU · tensorrt-llm
98/100
score
Throughput
492.8 tok/s
Latency (ITL)
2.0ms
Est. TTFT
0ms
Cost/Month
$4271
Cost/M Tokens
$3.30
BF16 · 1 GPU · tensorrt-llm
98/100
score
Throughput
492.8 tok/s
Latency (ITL)
2.0ms
Est. TTFT
0ms
Cost/Month
$6169
Cost/M Tokens
$4.76
BF16 · 2 GPUs · tensorrt-llm
95/100
score
Throughput
450.7 tok/s
Latency (ITL)
2.2ms
Est. TTFT
0ms
Cost/Month
$1879
Cost/M Tokens
$1.59
Deployment Options
API Deployment
openai
$4.40/M
output tokens
Single GPU
B100 SXM
$4271/mo
Min VRAM: 70 GB
Multi-GPU
H20 x2
450.7 tok/s
TP· $1879/mo
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| openai | $1.10 | $4.40 | Cheapest |
| openrouter | $1.10 | $4.40 |
Cost Analysis
| Provider | Input $/M | Output $/M | ~Monthly Cost |
|---|---|---|---|
| openaiBest Value | $1.10 | $4.40 | $28 |
| openrouter | $1.10 | $4.40 | $28 |
Cost per 1,000 Requests
Short (500 tok)
$1.43
via openai
Medium (2K tok)
$5.72
via openai
Long (8K tok)
$17.60
via openai
Performance Estimates
Throughput by GPU
VRAM Breakdown (B100 SXM, BF16)
Quality Benchmarks
Capabilities
Features
Supported Frameworks
Supported Precisions
Where to Deploy o3-mini
Self-Hosted Infrastructure
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Frequently Asked Questions
How much VRAM does o3-mini need for inference?
o3-mini requires approximately 140.0 GB of VRAM at BF16 precision, 70.0 GB at FP8, or 35.0 GB at INT4 quantization. Additional VRAM is needed for KV-cache (131072 bytes per token) and activations (~3.00 GB).
What is the best GPU for o3-mini?
The top recommended GPU for o3-mini is the B100 SXM using BF16 precision. It achieves approximately 492.8 tokens/sec at an estimated cost of $4271/month ($3.30/M tokens). Score: 98/100.
How much does o3-mini inference cost?
o3-mini API inference starts from $1.10/M input tokens and $4.40/M output tokens. Self-hosted inference costs depend on your GPU configuration — use our ROI calculator for a detailed breakdown.