o4-mini-deep-research vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare o4-mini-deep-research by OpenAI against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.
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o4-mini-deep-research
Budget ResearcherA cheaper deep-research model built on o4-mini for long searches and report drafting.
$2.00 / 1M
$8.00 / 1M
200K tokens
48.5%
Llama 5
Recursive Self-ImprovementMeta's flagship open model featuring native agent training, recursive self-improvement, and 5M token context.
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Key Differences at a Glance
- Llama 5 is 1.0x cheaper for API usage (input + output tokens combined).
- Llama 5 has a larger context window (5M vs 200K tokens).
- o4-mini-deep-research is faster at 70 tok/s vs 65 tok/s.
- Llama 5 leads on SWE-bench coding (74.0% vs 48.5%).
| Feature | o4-mini-deep-research | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 48 | 94 |
Provider | OpenAI | Meta |
Input Price Cost per million input tokens | $2.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $8.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.55 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 5M tokens~7,500 A4 pages of text |
Max Output Tokens Per single request | 100K | 128K |
Throughput Speed Tokens generated per second | 70 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 48.5% | 74% |
GPQA Diamond Graduate-level reasoning | 68% | 84% |
Cached Input Price Prompt caching discount per 1M tokens | $0.50 / 1M | $0.00 / 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.
o4-mini-deep-research · Strengths & Key Advantages
- Roughly five times cheaper than o3-deep-research
- Handles long multi-source research runs end to end
- Good enough for routine monitoring and briefs
- Good citation discipline for the price point
Considerations & Trade-offs
- Less depth and nuance in synthesis than o3-deep-research
- More prone to missing subtle contradictions across sources
Llama 5 · Strengths & Key Advantages
- 5 Million token context with native multi-audio/video understanding
- Recursive self-improvement training loop
- Completely open weights with enterprise commercial license
Considerations & Trade-offs
- Complex MoE serving requirements
- 4B tokenizer changes require updating legacy fine-tuning harnesses
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | o4-mini-deep-research | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.10 | $0.00 |
Output Tokens / Task Tokens generated per task | 75K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 30K | 20K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | o4-mini-deep-research | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | o4-mini-deep-research | meta-llama/Llama-5 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-06 | 2026-04 |
| Knowledge Cutoff | June 2024 | January 2026 |
| Architecture | Compact agentic reasoning transformer | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) |
| Parameters | Confidential | 600B+ total (~40-80B active) |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 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 ($2/1M in) across full 200K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.5/1M (no extra cache-write fee) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 372 GB VRAM (4-bit Quantized) · 1290 GB VRAM (Native BF16) |
| Safety & Frontier Classification | OpenAI Preparedness Framework Audited & System Card Published | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text, vision, video, audio |
| Output Modalities | text | text |
o4-mini-deep-research vs Llama 5: Quick Answers
Which is more intelligent, o4-mini-deep-research or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 48 on the composite Intelligence Index.
Which model is faster?
o4-mini-deep-research is faster, generating 70 tokens per second compared with 65 tokens per second.
Which model is cheaper to run?
Llama 5 is cheaper at $0.00 per 1M tokens blended, compared with $1.55 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 5 has lower latency, with a time to first token of 0.85s compared with 2.40s.
Which model has a larger context window?
Llama 5 has a larger context window, supporting 5M tokens compared with 200K tokens.
Verdict
Choose o4-mini-deep-research for high-volume research briefs where cost per run dominates, or choose Llama 5 for autonomous open-source multi-agent systems, deep software refactoring, and frontier research.
Frequently asked questions
Which is cheaper: o4-mini-deep-research or Llama 5?+
o4-mini-deep-research costs $2/1M input and $8/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: o4-mini-deep-research or Llama 5?+
o4-mini-deep-research supports 200K tokens whereas Llama 5 supports 5M tokens.
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.
Sources: OpenAI & Meta Official Documentation