Llama 3.3 70B vs GPT-4o mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Llama 3.3 70B by Meta and GPT-4o mini by OpenAI 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 3.3 70B holds a notable pricing advantage for token ingestion. On output generation, Llama 3.3 70B is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 128K tokens. In latency-sensitive production environments, Llama 3.3 70B 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 3.3 70B
Open-Weights TitanMeta's flagship open-weights foundation model delivering performance on par with previous 405B models at a fraction of the compute and hosting cost.
$0.00 / 1M
$0.00 / 1M
128K tokens
39.5%
GPT-4o mini
Compact UtilityOpenAI's compact, cost-efficient small model designed to replace GPT-3.5 Turbo across high-volume classification, extraction, and utility tasks.
$0.15 / 1M
$0.60 / 1M
128K tokens
24.5%
Key Differences at a Glance
- Llama 3.3 70B is 5.8x cheaper for API usage (input + output tokens combined).
- Llama 3.3 70B is faster at 280 tok/s vs 140 tok/s.
- Llama 3.3 70B leads on SWE-bench coding (39.5% vs 24.5%).
| Feature | Llama 3.3 70B | GPT-4o mini |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 38 | 30 |
Provider | Meta | OpenAI |
Input Price Cost per million input tokens | $0.00 / 1M | $0.15 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.60 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.07 / 1M | $0.14 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 4K | 16K |
Throughput Speed Tokens generated per second | 280 tok/s | 140 tok/s |
SWE-bench Verified Real-world software engineering | 39.5% | 24.5% |
GPQA Diamond Graduate-level reasoning | 48.9% | 40.2% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 1M | $0.07 / 1M |
Who Wins Where?
At-a-glance decision frameworkEmpirical Head-to-Head Benchmark Scores
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.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
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.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 3.3 70B · Strengths & Key Advantages
- 100% open-weights allowing full data sovereignty, private hosting, and fine-tuning
- Lightning-fast throughput (up to 280 tok/s on specialized hardware inference providers)
- Economical cloud API pricing ($0.20 input / $0.70 output per million tokens)
- Industry standard for local offline AI and enterprise compliance
Considerations & Trade-offs
- Knowledge cutoff in late 2023 requires RAG for recent events
- Smaller maximum output token generation limit (4K tokens)
GPT-4o mini · Strengths & Key Advantages
- Economical pricing ($0.15 in / $0.60 out per 1M tokens)
- Full vision multimodal support at low cost
- High output token limit (16K tokens)
- Consistent function calling and JSON output reliability
Considerations & Trade-offs
- Limited reasoning for complex software architecture
- Slightly higher pricing than Gemini 2.0 Flash ($0.15 vs $0.10)
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 3.3 70B | GPT-4o mini |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.21 | $0.16 |
Output Tokens / Task Tokens generated per task | 45K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 3.3 70B | GPT-4o mini |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-3.3-70B | gpt-4o-mini |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2024-12 | 2024-07 |
| Knowledge Cutoff | December 2023 | October 2023 |
| Architecture | Dense Autoregressive Transformer | Small Multimodal Dense |
| Parameters | 70B | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($0.15/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix caching at $0.075/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| 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) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text, vision |
| Output Modalities | text | text |
Llama 3.3 70B vs GPT-4o mini: Quick Answers
Which is more intelligent, Llama 3.3 70B or GPT-4o mini?
Llama 3.3 70B is more intelligent. It scores 38 compared with 30 on the composite Intelligence Index.
Which model is faster?
Llama 3.3 70B is faster, generating 280 tokens per second compared with 140 tokens per second.
Which model is cheaper to run?
Llama 3.3 70B is cheaper at $0.07 per 1M tokens blended, compared with $0.14 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 3.3 70B has lower latency, with a time to first token of 0.25s compared with 0.31s.
Verdict
Verdict & Model Selection Recommendation: Choose Llama 3.3 70B if your priority is Private on-premises deployments, high-throughput text summarization, and fine-tuned domain tasks. Its performance in demanding tasks justifies its operational token cost. Choose GPT-4o mini if your workflow prioritizes Data extraction, customer support routing, and lightweight web scraping. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Frequently asked questions
How does Llama 3.3 70B compare to GPT-4o mini in API token pricing and cost per token?+
Llama 3.3 70B costs $0.00 per 1M input tokens and $0.00 per 1M output tokens, whereas GPT-4o mini costs $0.15 input and $0.60 output. Overall, Llama 3.3 70B 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 3.3 70B or GPT-4o mini?+
Both Llama 3.3 70B and GPT-4o mini offer an identical context window of 128K tokens (~96k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Llama 3.3 70B or GPT-4o mini?+
Llama 3.3 70B scores 39.5% on SWE-bench Verified while GPT-4o mini scores 24.5%. For autonomous software engineering and multi-file debugging, Llama 3.3 70B holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Llama 3.3 70B and GPT-4o mini?+
Llama 3.3 70B is optimized for low-latency direct generation. In contrast, GPT-4o mini focuses on standard fast inference.
How does inference speed (tokens per second) compare between Llama 3.3 70B and GPT-4o mini?+
Llama 3.3 70B generates approximately 280 tokens per second, while GPT-4o mini delivers 140 tokens per second. In latency-sensitive production environments, Llama 3.3 70B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Llama 3.3 70B and GPT-4o mini to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Llama 3.3 70B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-4o mini. 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.
Related reading
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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.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs