Llama 3.1 Nemotron 70B Instruct vs Mistral Medium 3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Llama 3.1 Nemotron 70B Instruct by NVIDIA and Mistral Medium 3 by Mistral AI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Llama 3.1 Nemotron 70B Instruct holds a notable pricing advantage for token ingestion. On output generation, Llama 3.1 Nemotron 70B Instruct is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Mistral Medium 3 provides an expanded memory horizon with 131K tokens. In latency-sensitive production environments, Mistral Medium 3 leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Llama 3.1 Nemotron 70B Instruct
Modello a Pesi ApertiLlama 3.1 Nemotron 70B Instruct di NVIDIA è un modello di intelligenza artificiale con finestra di contesto da 128K token e tariffazione API di $0.35/1M token in input e $0.7/1M in output, ideale per coding, ragionamento e automazione azien
$0.35 / 1M
$0.70 / 1M
128K tokens
41.2%
Mistral Medium 3
Modello a Pesi ApertiMistral Medium 3 di Mistral AI è un modello di intelligenza artificiale con finestra di contesto da 131K token e tariffazione API di $0.4/1M token in input e $2/1M in output, ideale per coding, ragionamento e automazione aziendale.
$0.40 / 1M
$2.00 / 1M
131.1K tokens
40.5%
Differenze Chiave a Colpo d’Occhio
- Llama 3.1 Nemotron 70B Instruct is 2.0x cheaper for API usage (input + output tokens combined).
- Mistral Medium 3 has a larger context window (131K vs 128K tokens).
- Mistral Medium 3 is faster at 85 tok/s vs 80 tok/s.
- Llama 3.1 Nemotron 70B Instruct leads on SWE-bench coding (41.2% vs 40.5%).
| Feature | Llama 3.1 Nemotron 70B Instruct | Mistral Medium 3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 47 | 47 |
Provider | NVIDIA | Mistral AI |
Input Price Cost per million input tokens | $0.35 / 1M | $0.40 / 1M |
Output Price Cost per million output tokens | $0.70 / 1M | $2.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.21 / 1M | $0.35 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 8K | 16K |
Throughput Speed Tokens generated per second | 80 tok/s | 85 tok/s |
SWE-bench Verified Real-world software engineering | 41.2% | 40.5% |
GPQA Diamond Graduate-level reasoning | 58.5% | 57.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 1M | $0.10 / 1M |
Chi vince in ogni categoria?
At-a-glance decision frameworkPunteggi Benchmark Verificati
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 3.1 Nemotron 70B Instruct · Strengths & Key Advantages
- Architettura RLHF Aligned Llama 3.1 Derivative avanzata di NVIDIA con supporto fino a 128K token di contesto
- Ottimo rapporto qualità-prezzo ($0.35/1M input, $0.7/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
Mistral Medium 3 · Strengths & Key Advantages
- Architettura Enterprise Multimodal Transformer avanzata di Mistral AI con supporto fino a 131K token di contesto
- Ottimo rapporto qualità-prezzo ($0.4/1M input, $2/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 3.1 Nemotron 70B Instruct | Mistral Medium 3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.28 | $0.35 |
Output Tokens / Task Tokens generated per task | 32K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 3.1 Nemotron 70B Instruct | Mistral Medium 3 |
|---|---|---|
| Official API Model ID (SDK) | llama-3-1-nemotron-70b | mistral-medium-3-latest |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-04 | 2025-05 |
| Knowledge Cutoff | December 2024 | March 2025 |
| Architecture | RLHF Aligned Llama 3.1 Derivative | Enterprise Multimodal Transformer |
| Parameters | 70B | Confidential (~70B) |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($0.35/1M in) across full 128K window | Flat rate ($0.4/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Cached input at $0.1/1M tokens | Cached input at $0.1/1M tokens |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text, vision |
| Output Modalities | text | text |
Llama 3.1 Nemotron 70B Instruct vs Mistral Medium 3: Risposte Rapide (FAQ)
Which model is faster?
Mistral Medium 3 is faster, generating 85 tokens per second compared with 80 tokens per second.
Which model is cheaper to run?
Llama 3.1 Nemotron 70B Instruct is cheaper at $0.21 per 1M tokens blended, compared with $0.35 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 3.1 Nemotron 70B Instruct has lower latency, with a time to first token of 0.45s compared with 0.48s.
Which model has a larger context window?
Mistral Medium 3 has a larger context window, supporting 131.1K tokens compared with 128K tokens.
Verdetto
Verdict & Model Selection Recommendation: Choose Llama 3.1 Nemotron 70B Instruct if your priority is Enterprise customer support, synthetic data generation, and local agent orchestration. Its performance in demanding tasks justifies its operational token cost. Choose Mistral Medium 3 if your workflow prioritizes Enterprise internal assistants, document workflow automation, and multimodal analytics. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Domande Frequenti (FAQ)
How does Llama 3.1 Nemotron 70B Instruct compare to Mistral Medium 3 in API token pricing and cost per token?+
Llama 3.1 Nemotron 70B Instruct costs $0.35 per 1M input tokens and $0.70 per 1M output tokens, whereas Mistral Medium 3 costs $0.40 input and $2.00 output. Overall, Llama 3.1 Nemotron 70B Instruct is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Llama 3.1 Nemotron 70B Instruct or Mistral Medium 3?+
Llama 3.1 Nemotron 70B Instruct supports a context window of 128K tokens (~96k words), while Mistral Medium 3 supports 131K tokens (~98k words). Therefore, Mistral Medium 3 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Llama 3.1 Nemotron 70B Instruct or Mistral Medium 3?+
Llama 3.1 Nemotron 70B Instruct scores 41.2% on SWE-bench Verified while Mistral Medium 3 scores 40.5%. For autonomous software engineering and multi-file debugging, Llama 3.1 Nemotron 70B Instruct holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Llama 3.1 Nemotron 70B Instruct and Mistral Medium 3?+
Llama 3.1 Nemotron 70B Instruct is optimized for low-latency direct generation. In contrast, Mistral Medium 3 focuses on standard fast inference.
How does inference speed (tokens per second) compare between Llama 3.1 Nemotron 70B Instruct and Mistral Medium 3?+
Llama 3.1 Nemotron 70B Instruct generates approximately 80 tokens per second, while Mistral Medium 3 delivers 85 tokens per second. In latency-sensitive production environments, Mistral Medium 3 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Llama 3.1 Nemotron 70B Instruct and Mistral Medium 3 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Llama 3.1 Nemotron 70B Instruct to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Mistral Medium 3. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
Fonti ufficiali: GetInfoUs Independent AI Benchmarks & Official API Specs