Mistral Large 3 Mistral AI
💰 Total Cost Calculation (from Plugin)
Output: $0.000188
Output: $0.000188
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 100,000 input tokens and 500 output tokens:
- Input Cost: $0.012500 (rounded ~ $0.01)
- Output Cost: $0.000188
- Total Cost: $0.008188 (rounded ~ $0.01)
- Cost per 1K tokens: $0.000081
- Tokens per dollar: 12,274,809 tokens
- Context Window: 256000 tokens
Speed & Performance Analysis
With a processing speed of 500 tokens per second and 160ms time to first token:
- Processing Time: 3 minutes, 27.21 seconds
- Latency: 160 milliseconds to first token
- Base Throughput: 500 tokens/second
- Effective Throughput: 485 tokens/second (temperature-adjusted)
Best Use Cases
Want this applied to YOUR actual stack?
This calculator shows the math for Mistral Large 3. Your decision needs more — current infrastructure, compliance requirements, actual workload patterns, volume tiers — that change which model is right for you.
Get a $39 personalized AI Architecture Audit. PDF tailored to your stack, delivered in under 60 seconds. 7-day no-questions-asked refund.
Get my instant AI audit — $39 →✨ Market Recommendations AI Model Registry
← Back to Mistral Large 3| Rank | AI Model & Provider | Total Cost | vs Mistral Large 3 |
|---|---|---|---|
| 🏆 |
Mistral Small 3
Mistral AI
|
$0.001638 Best Value | ↓ 80% cheaper |
| 🥈 |
Gemini 3.1 Flash Lite
Google
|
$0.004188 | ↓ 48.9% cheaper |
| 🥉 |
Gemini 3.5 Flash-Lite
Google
|
$0.005113 (rounded ~ $0.01) | ↓ 37.6% cheaper |
| #4 |
Gemini 2.5 Flash
Google
|
$0.005113 (rounded ~ $0.01) | ↓ 37.6% cheaper |
| #5 |
Gemini 3.8 Flash
Google
|
$0.012469 (rounded ~ $0.01) | ↑ 52.3% more |
| #6 |
GPT-5.4 mini
OpenAI
|
$0.012563 (rounded ~ $0.01) | ↑ 53.4% more |
| #7 |
o4-mini Deep Research
OpenAI
|
$0.016500 (rounded ~ $0.02) | ↑ 101.5% more |
| #8 |
Claude Haiku 4.5
Anthropic
|
$0.016625 (rounded ~ $0.02) | ↑ 103.1% more |
| #9 |
Gemini 3.1 Flash
Google
|
$0.016750 (rounded ~ $0.02) | ↑ 104.6% more |
| #10 |
GPT-5.6 Luna
OpenAI
|
$0.016750 (rounded ~ $0.02) | ↑ 104.6% more |
| #11 |
o4-mini
OpenAI
|
$0.018150 (rounded ~ $0.02) | ↑ 121.7% more |
| #12 |
Gemini 3.6 Flash
Google
|
$0.024938 (rounded ~ $0.02) | ↑ 204.6% more |
| #13 |
Gemini 3.5 Flash
Google
|
$0.025125 (rounded ~ $0.03) | ↑ 206.9% more |
| #14 |
GPT-5.3 Codex Spark
OpenAI
|
$0.029750 | ↑ 263.4% more |
| #15 |
GPT-5.3 Instant
OpenAI
|
$0.029750 | ↑ 263.4% more |
| #16 |
Claude Sonnet 5
Anthropic
|
$0.033250 (rounded ~ $0.03) | ↑ 306.1% more |
| #17 |
GPT-5.6 Terra
OpenAI
|
$0.041875 (rounded ~ $0.04) | ↑ 411.5% more |
| #18 |
Gemini 2.5 Pro
Google
|
$0.042500 (rounded ~ $0.04) | ↑ 419.1% more |
| #19 |
Claude Sonnet 4.6
Anthropic
|
$0.049875 | ↑ 509.2% more |
| #20 |
Grok 4.3
xAI
|
$0.065000 (rounded ~ $0.07) | ↑ 693.9% more |
| #21 |
Grok 4.20 Beta
xAI
|
$0.065000 (rounded ~ $0.07) | ↑ 693.9% more |
| #22 |
Gemini 3.1 Pro
Google
|
$0.067000 (rounded ~ $0.07) | ↑ 718.3% more |
| #23 |
Claude Opus 4.7
Anthropic
|
$0.083125 (rounded ~ $0.08) | ↑ 915.3% more |
| #24 |
Claude Opus 5
Anthropic
|
$0.083125 (rounded ~ $0.08) | ↑ 915.3% more |
| #25 |
Claude Opus 4.8
Anthropic
|
$0.083125 (rounded ~ $0.08) | ↑ 915.3% more |
| #26 |
Claude Opus 4.6
Anthropic
|
$0.083125 (rounded ~ $0.08) | ↑ 915.3% more |
| #27 |
GPT-5.4
OpenAI
|
$0.083750 (rounded ~ $0.08) | ↑ 922.9% more |
| #28 |
GPT-5.4 Thinking
OpenAI
|
$0.083750 (rounded ~ $0.08) | ↑ 922.9% more |
| #29 |
GPT-5.5 Instant
OpenAI
|
$0.083750 (rounded ~ $0.08) | ↑ 922.9% more |
| #30 |
GPT-5.6 Sol
OpenAI
|
$0.083750 (rounded ~ $0.08) | ↑ 922.9% more |
| #31 |
Claude Fable 5.1
Anthropic
|
$0.158750 (rounded ~ $0.16) | ↑ 1838.9% more |
| #32 |
Claude Mythos 5.1
Anthropic
|
$0.158750 (rounded ~ $0.16) | ↑ 1838.9% more |
| #33 |
o3 Deep Research
OpenAI
|
$0.165000 (rounded ~ $0.17) | ↑ 1915.3% more |
| #34 |
Claude Fable 5
Anthropic
|
$0.166250 (rounded ~ $0.17) | ↑ 1930.5% more |
| #35 |
Claude Mythos 5
Anthropic
|
$0.166250 (rounded ~ $0.17) | ↑ 1930.5% more |
| #36 |
GPT-5.5
OpenAI
|
$0.167500 (rounded ~ $0.17) | ↑ 1945.8% more |
| #37 |
o3 Pro
OpenAI
|
$0.330000 | ↑ 3930.5% more |
| #38 |
GPT-6 Astra
OpenAI
|
$0.332500 (rounded ~ $0.33) | ↑ 3961.1% more |
| #39 |
GPT-5.2 Pro
OpenAI
|
$0.357000 (rounded ~ $0.36) | ↑ 4260.3% more |
| #40 |
GPT-5.2 Pro
OpenAI
|
$0.357000 (rounded ~ $0.36) | ↑ 4260.3% more |
Mistral Small 3 Mistral AI
Gemini 3.1 Flash Lite Google
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Gemini 3.8 Flash Google
GPT-5.4 mini OpenAI
o4-mini Deep Research OpenAI
Claude Haiku 4.5 Anthropic
Gemini 3.1 Flash Google
GPT-5.6 Luna OpenAI
o4-mini OpenAI
Gemini 3.6 Flash Google
Gemini 3.5 Flash Google
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
Claude Sonnet 5 Anthropic
GPT-5.6 Terra OpenAI
Gemini 2.5 Pro Google
Claude Sonnet 4.6 Anthropic
Grok 4.3 xAI
Grok 4.20 Beta xAI
Gemini 3.1 Pro Google
Claude Opus 4.7 Anthropic
Claude Opus 5 Anthropic
Claude Opus 4.8 Anthropic
Claude Opus 4.6 Anthropic
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
GPT-5.5 Instant OpenAI
GPT-5.6 Sol OpenAI
Claude Fable 5.1 Anthropic
Claude Mythos 5.1 Anthropic
o3 Deep Research OpenAI
Claude Fable 5 Anthropic
Claude Mythos 5 Anthropic
GPT-5.5 OpenAI
o3 Pro OpenAI
GPT-6 Astra OpenAI
GPT-5.2 Pro OpenAI
GPT-5.2 Pro OpenAI
Deploying Mistral Large 3 for 100,000-Token Legal Review Pipelines offers a compelling balance for educational content creators focused on cost-effective, sovereign, or specialized legal AI deployments. Legal review at this scale is often performed in bursts—high-intensity discovery phases followed by periods of dormancy. This model excels in these episodic workloads, providing high-quality extraction and summarization that rivals larger frontier models.
In an educational context, use this model to demonstrate how smaller high-performance architectures can be optimized for specific domains. It is particularly well-suited for curriculum that covers fine-tuning or RAG strategies, where you are teaching students to build bespoke legal assistants that operate on private, sensitive data.
Because legal documentation is often highly repetitive, the model’s efficiency allows developers to iterate rapidly on their prompts and extraction schemas without the overhead of massive compute costs. When building your course, focus on how this model handles the structural hierarchy of legal documents. It is uniquely capable of identifying sections, subsections, and attachments, which is critical for automating the creation of contract summaries. This makes it an ideal workhorse model for students learning to build end-to-end contract management systems that prioritize performance and ease of integration into existing professional workflows.