GPT-4o (08-06) vs Llama 3.3 70B Instruct

Name: GPT-4o (08-06)
Author: OpenAI

GPT-4o (08-06) (2024) and Llama 3.3 70B Instruct (2024) are compact production models from OpenAI and AI at Meta. Both ship a 128K-token context window. On MMLU PRO, GPT-4o (08-06) leads by 5.8 pts. On pricing, Llama 3.3 70B Instruct costs $0.14/1M input tokens versus $2.50/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.

Llama 3.3 70B Instruct is ~1752% cheaper at $0.14/1M; pay for GPT-4o (08-06) only for coding workflow support.

Decision scorecard

Local evidence first

Signal	GPT-4o (08-06)	Llama 3.3 70B Instruct	How to read it
Best for	multimodal apps and provider-routed production	provider-routed production	Use-case synthesis from product type, capability flags, context, and provider data.
Decision fit	Coding, RAG, and Agents	RAG, Long context, and Classification	Primary workload tags from local decision data.
Context window	128K	128K	Higher is better when prompts, retrieval chunks, or transcripts are large.
Cheapest output	$10/1M tokens	$0.40/1M tokens	Cheapest tracked provider route; verify your exact region and tier.
Provider routes	3 tracked	13 tracked	Broader coverage can reduce vendor lock-in and fallback risk.
Shared benchmarks	MMLU PRO leader	1 shared	Visible benchmark lead is 5.8 points on MMLU PRO.

Decision tradeoffs

Choose GPT-4o (08-06) when...

GPT-4o (08-06) holds a shared-benchmark lead on MMLU PRO, ahead by 5.8 points.
GPT-4o (08-06) uniquely exposes Vision, Multimodal, and Code execution in local model data.
Local decision data tags GPT-4o (08-06) for Coding, RAG, and Agents.

Choose Llama 3.3 70B Instruct when...

Llama 3.3 70B Instruct has the lower cheapest tracked output price at $0.40/1M tokens.
Llama 3.3 70B Instruct has broader tracked provider coverage for fallback and route flexibility.
Local decision data tags Llama 3.3 70B Instruct for RAG, Long context, and Classification.

Monthly cost at traffic

Estimate token spend from the cheapest tracked input and output route or tier on this page.

Lower estimate Llama 3.3 70B Instruct

Requests / monthInput tokens / requestOutput tokens / request

GPT-4o (08-06)

$4,500

Cheapest tracked route/tier: OpenAI API

Llama 3.3 70B Instruct

$208

Cheapest tracked route/tier: Novita AI

Estimated monthly gap: $4,292. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.

Switch friction

GPT-4o (08-06) -> Llama 3.3 70B Instruct

Provider overlap exists on OpenRouter; start route-level A/B tests there.
Llama 3.3 70B Instruct is $9.60/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
Check replacement coverage for Vision, Multimodal, and Code execution before moving production traffic.

Llama 3.3 70B Instruct -> GPT-4o (08-06)

Provider overlap exists on OpenRouter; start route-level A/B tests there.
GPT-4o (08-06) is $9.60/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
GPT-4o (08-06) adds Vision, Multimodal, and Code execution in local capability data.

Specs

Specification	GPT-4o (08-06) OpenAI	Llama 3.3 70B Instruct AI at Meta
Released	2024-08-06	2024-12-06
Context window	128K	128K
Parameters	1.76T (8x222B MoE)*	70B
Architecture	Mixture of Experts	Decoder Only
License	Proprietary	Llama 3.3 Community
Openness	Proprietary	Open weights
Weights	Not released	Available
Code	Unknown	Unknown
Commercial use	Restricted	Restricted
Knowledge cutoff	2023-10	2023-12

Pricing and availability

Pricing attribute	GPT-4o (08-06)	Llama 3.3 70B Instruct
Input price	$2.50/1M tokens	$0.14/1M tokens
Output price	$10/1M tokens	$0.40/1M tokens
Providers	OpenAI API Salesforce Models API OpenRouter	Cloudflare Workers AI Fireworks AI NVIDIA NIM GroqCloud Together AI AWS Bedrock

Capabilities

Capability	GPT-4o (08-06)	Llama 3.3 70B Instruct
Vision	Yes	No
Multimodal	Yes	No
Reasoning	No	No
JSON / Tool use	No	No
Structured outputs	Yes	Yes
Code execution	Yes	No
IDE integration	No	No
Computer use	No	No
Parallel agents	No	No

Benchmarks

Benchmark	GPT-4o (08-06)	Llama 3.3 70B Instruct
MMLU PRO	74.7	68.9

Deep dive

On shared benchmark coverage, MMLU PRO has GPT-4o (08-06) at 74.7 and Llama 3.3 70B Instruct at 68.9, with GPT-4o (08-06) ahead by 5.8 points. The largest visible gap is 5.8 points on MMLU PRO, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.

The capability footprint differs most on vision: GPT-4o (08-06), multimodal input: GPT-4o (08-06), and code execution: GPT-4o (08-06). Both models share structured outputs, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.

For cost, GPT-4o (08-06) lists $2.50/1M input and $10/1M output tokens on the cheapest tracked provider, while Llama 3.3 70B Instruct lists $0.14/1M input and $0.40/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.3 70B Instruct lower by about $4.54 per million blended tokens. Availability is 3 providers versus 13, so concentration risk also matters.

Choose GPT-4o (08-06) when coding workflow support are central to the workload. Choose Llama 3.3 70B Instruct when provider fit, lower input-token cost, and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.

FAQ

Which has a larger context window, GPT-4o (08-06) or Llama 3.3 70B Instruct?

GPT-4o (08-06) supports 128K tokens, while Llama 3.3 70B Instruct supports 128K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.

Which is cheaper, GPT-4o (08-06) or Llama 3.3 70B Instruct?

Llama 3.3 70B Instruct is cheaper on tracked token pricing. GPT-4o (08-06) costs $2.50/1M input and $10/1M output tokens. Llama 3.3 70B Instruct costs $0.14/1M input and $0.40/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is GPT-4o (08-06) or Llama 3.3 70B Instruct open source?

GPT-4o (08-06) is listed under Proprietary. Llama 3.3 70B Instruct is listed under Llama 3.3 Community. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.

Which is better for vision, GPT-4o (08-06) or Llama 3.3 70B Instruct?

GPT-4o (08-06) has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

Which is better for multimodal input, GPT-4o (08-06) or Llama 3.3 70B Instruct?

GPT-4o (08-06) has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Where can I run GPT-4o (08-06) and Llama 3.3 70B Instruct?

GPT-4o (08-06) is available on OpenAI API, Salesforce Models API, and OpenRouter. Llama 3.3 70B Instruct is available on Cloudflare Workers AI, Fireworks AI, NVIDIA NIM, GroqCloud, and Together AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

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Last reviewed: 2026-10-17. Data sourced from public model cards and provider documentation.

Both models

GPT-4o (08-06)Llama 3.3 70B Instruct