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GPT-4 vs Llama Guard 7B

GPT-4 (2023) and Llama Guard 7B (2023) are compact production models from OpenAI and AI at Meta. GPT-4 ships a 8K-token context window, while Llama Guard 7B ships a 2K-token context window. On pricing, Llama Guard 7B costs $0.2/1M input tokens versus $30/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.

Llama Guard 7B is ~14900% cheaper at $0.2/1M; pay for GPT-4 only for coding workflow support.

Decision scorecard

Local evidence first
SignalGPT-4Llama Guard 7B
Decision fitCoding, Agents, and VisionClassification and JSON / Tool use
Context window8K2K
Cheapest output$60/1M tokens$0.2/1M tokens
Provider routes4 tracked3 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose GPT-4 when...
  • GPT-4 has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • GPT-4 has broader tracked provider coverage for fallback and procurement flexibility.
  • GPT-4 uniquely exposes Vision, Multimodal, and Function calling in local model data.
  • Local decision data tags GPT-4 for Coding, Agents, and Vision.
Choose Llama Guard 7B when...
  • Llama Guard 7B has the lower cheapest tracked output price at $0.2/1M tokens.
  • Local decision data tags Llama Guard 7B for Classification and JSON / Tool use.

Monthly cost at traffic

Estimate token spend from the cheapest tracked input and output prices on this page.

Lower estimate Llama Guard 7B

GPT-4

$39,000

Cheapest tracked route: OpenAI API

Llama Guard 7B

$210

Cheapest tracked route: Together AI

Estimated monthly gap: $38,790. Batch, cache, and negotiated pricing are excluded from this local estimate.

Switch friction

GPT-4 -> Llama Guard 7B
  • No overlapping tracked provider route is sourced for GPT-4 and Llama Guard 7B; plan for SDK, billing, or endpoint changes.
  • Llama Guard 7B is $59.80/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
  • Check replacement coverage for Vision, Multimodal, and Function calling before moving production traffic.
Llama Guard 7B -> GPT-4
  • No overlapping tracked provider route is sourced for Llama Guard 7B and GPT-4; plan for SDK, billing, or endpoint changes.
  • GPT-4 is $59.80/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
  • GPT-4 adds Vision, Multimodal, and Function calling in local capability data.

Specs

Specification
Released2023-03-142023-12-07
Context window8K2K
Parameters1.76T (8x222B MoE)*7B
Architecturemixture of expertsdecoder only
LicenseProprietaryOpen Source
Knowledge cutoff2021-09-

Pricing and availability

Pricing attributeGPT-4Llama Guard 7B
Input price$30/1M tokens$0.2/1M tokens
Output price$60/1M tokens$0.2/1M tokens
Providers

Capabilities

CapabilityGPT-4Llama Guard 7B
VisionYesNo
MultimodalYesNo
ReasoningNoNo
Function callingYesNo
Tool useNoNo
Structured outputsYesYes
Code executionYesNo

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on vision: GPT-4, multimodal input: GPT-4, function calling: GPT-4, and code execution: GPT-4. 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-4 lists $30/1M input and $60/1M output tokens, while Llama Guard 7B lists $0.2/1M input and $0.2/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama Guard 7B lower by about $38.80 per million blended tokens. Availability is 4 providers versus 3, so concentration risk also matters.

Choose GPT-4 when coding workflow support, larger context windows, and broader provider choice are central to the workload. Choose Llama Guard 7B when provider fit and lower input-token cost are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.

FAQ

Which has a larger context window, GPT-4 or Llama Guard 7B?

GPT-4 supports 8K tokens, while Llama Guard 7B supports 2K 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-4 or Llama Guard 7B?

Llama Guard 7B is cheaper on tracked token pricing. GPT-4 costs $30/1M input and $60/1M output tokens. Llama Guard 7B costs $0.2/1M input and $0.2/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is GPT-4 or Llama Guard 7B open source?

GPT-4 is listed under Proprietary. Llama Guard 7B is listed under Open Source. 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-4 or Llama Guard 7B?

GPT-4 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-4 or Llama Guard 7B?

GPT-4 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-4 and Llama Guard 7B?

GPT-4 is available on OpenAI API, Azure OpenAI, Salesforce Einstein Generative AI, and OpenRouter. Llama Guard 7B is available on Cloudflare Workers AI, Together AI, and Fireworks AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

Continue comparing

Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.