GPT-4 Turbo vs Llama 2 13B Chat
GPT-4 Turbo (2024) and Llama 2 13B Chat (2023) are compact production models from OpenAI and AI at Meta. GPT-4 Turbo ships a 128K-token context window, while Llama 2 13B Chat ships a 4K-token context window. On Massive Multitask Language Understanding, GPT-4 Turbo leads by 15.3 pts. On pricing, Llama 2 13B Chat costs $0.1/1M input tokens versus $5/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 2 13B Chat is ~4900% cheaper at $0.1/1M; pay for GPT-4 Turbo only for coding workflow support.
Decision scorecard
Local evidence first| Signal | GPT-4 Turbo | Llama 2 13B Chat |
|---|---|---|
| Decision fit | Coding, RAG, and Agents | Coding, Classification, and JSON / Tool use |
| Context window | 128K | 4K |
| Cheapest output | $15/1M tokens | $0.5/1M tokens |
| Provider routes | 5 tracked | 12 tracked |
| Shared benchmarks | Massive Multitask Language Understanding leader | 1 rows |
Decision tradeoffs
- GPT-4 Turbo leads the largest shared benchmark signal on Massive Multitask Language Understanding by 15.3 points.
- GPT-4 Turbo has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GPT-4 Turbo uniquely exposes Vision, Multimodal, and Function calling in local model data.
- Local decision data tags GPT-4 Turbo for Coding, RAG, and Agents.
- Llama 2 13B Chat has the lower cheapest tracked output price at $0.5/1M tokens.
- Llama 2 13B Chat has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Llama 2 13B Chat for Coding, Classification, and JSON / Tool use.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
GPT-4 Turbo
$7,750
Cheapest tracked route: Replicate API
Llama 2 13B Chat
$205
Cheapest tracked route: Replicate API
Estimated monthly gap: $7,545. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Replicate API; start route-level A/B tests there.
- Llama 2 13B Chat is $14.50/1M 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.
- Provider overlap exists on Replicate API; start route-level A/B tests there.
- GPT-4 Turbo is $14.50/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- GPT-4 Turbo adds Vision, Multimodal, and Function calling in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-04-09 | 2023-07-18 |
| Context window | 128K | 4K |
| Parameters | 1.76T (8x222B MoE)* | 13B |
| Architecture | mixture of experts | decoder only |
| License | Proprietary | Open Source |
| Knowledge cutoff | 2023-12 | 2022-09 |
Pricing and availability
| Pricing attribute | GPT-4 Turbo | Llama 2 13B Chat |
|---|---|---|
| Input price | $5/1M tokens | $0.1/1M tokens |
| Output price | $15/1M tokens | $0.5/1M tokens |
| Providers |
Capabilities
| Capability | GPT-4 Turbo | Llama 2 13B Chat |
|---|---|---|
| Vision | Yes | No |
| Multimodal | Yes | No |
| Reasoning | No | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | Yes |
| Code execution | Yes | No |
Benchmarks
| Benchmark | GPT-4 Turbo | Llama 2 13B Chat |
|---|---|---|
| Massive Multitask Language Understanding | 86.5 | 71.2 |
Deep dive
On shared benchmark coverage, Massive Multitask Language Understanding has GPT-4 Turbo at 86.5 and Llama 2 13B Chat at 71.2, with GPT-4 Turbo ahead by 15.3 points. The largest visible gap is 15.3 points on Massive Multitask Language Understanding, 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-4 Turbo, multimodal input: GPT-4 Turbo, function calling: GPT-4 Turbo, tool use: GPT-4 Turbo, and code execution: GPT-4 Turbo. 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 Turbo lists $5/1M input and $15/1M output tokens, while Llama 2 13B Chat lists $0.1/1M input and $0.5/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 2 13B Chat lower by about $7.78 per million blended tokens. Availability is 5 providers versus 12, so concentration risk also matters.
Choose GPT-4 Turbo when coding workflow support and larger context windows are central to the workload. Choose Llama 2 13B Chat 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-4 Turbo or Llama 2 13B Chat?
GPT-4 Turbo supports 128K tokens, while Llama 2 13B Chat supports 4K 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 Turbo or Llama 2 13B Chat?
Llama 2 13B Chat is cheaper on tracked token pricing. GPT-4 Turbo costs $5/1M input and $15/1M output tokens. Llama 2 13B Chat costs $0.1/1M input and $0.5/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GPT-4 Turbo or Llama 2 13B Chat open source?
GPT-4 Turbo is listed under Proprietary. Llama 2 13B Chat 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 Turbo or Llama 2 13B Chat?
GPT-4 Turbo 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 Turbo or Llama 2 13B Chat?
GPT-4 Turbo 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 Turbo and Llama 2 13B Chat?
GPT-4 Turbo is available on OpenAI API, Azure OpenAI, Salesforce Einstein Generative AI, OpenRouter, and Replicate API. Llama 2 13B Chat is available on Alibaba Cloud PAI-EAS, AWS Bedrock, Microsoft Foundry, GCP Vertex AI, and Cloudflare Workers AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.