Claude 1.3 vs GPT-4 Vision Preview
Claude 1.3 (2023) and GPT-4 Vision Preview (2023) are compact production models from Anthropic and OpenAI. Claude 1.3 ships a 100K-token context window, while GPT-4 Vision Preview ships a 128K-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing. The goal is to make the tradeoff clear before deeper testing.
GPT-4 Vision Preview is safer overall; choose Claude 1.3 when provider fit matters.
Decision scorecard
Local evidence first| Signal | Claude 1.3 | GPT-4 Vision Preview |
|---|---|---|
| Best for | general production evaluation | multimodal apps |
| Decision fit | General | Coding, Agents, and Long context |
| Context window | 100K | 128K |
| Cheapest output | - | $40/1M tokens |
| Provider routes | 0 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Use Claude 1.3 when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
- GPT-4 Vision Preview has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GPT-4 Vision Preview has broader tracked provider coverage for fallback and procurement flexibility.
- GPT-4 Vision Preview uniquely exposes Vision, Multimodal, and Code execution in local model data.
- Local decision data tags GPT-4 Vision Preview for Coding, Agents, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Claude 1.3
Unavailable
No complete token price in local provider data
GPT-4 Vision Preview
$18,000
Cheapest tracked route/tier: Azure OpenAI
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Claude 1.3 and GPT-4 Vision Preview; plan for SDK, billing, or endpoint changes.
- GPT-4 Vision Preview adds Vision, Multimodal, and Code execution in local capability data.
- No overlapping tracked provider route is sourced for GPT-4 Vision Preview and Claude 1.3; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision, Multimodal, and Code execution before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2023-04-14 | 2023-11-06 |
| Context window | 100K | 128K |
| Parameters | — | 1.76T (8x222B MoE)* |
| Architecture | decoder only | mixture of experts |
| License | Unknown | Proprietary |
| Knowledge cutoff | - | 2023-04 |
Pricing and availability
| Pricing attribute | Claude 1.3 | GPT-4 Vision Preview |
|---|---|---|
| Input price | - | $10/1M tokens |
| Output price | - | $40/1M tokens |
| Providers | - |
Capabilities
| Capability | Claude 1.3 | GPT-4 Vision Preview |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | No |
| Code execution | No | Yes |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: GPT-4 Vision Preview, multimodal input: GPT-4 Vision Preview, and code execution: GPT-4 Vision Preview. Both models share the core language-model surface, 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.
Pricing coverage is uneven: Claude 1.3 has no token price sourced yet and GPT-4 Vision Preview has $10/1M input tokens. Provider availability is 0 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Claude 1.3 when provider fit are central to the workload. Choose GPT-4 Vision Preview when coding workflow support, larger context windows, 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. 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, Claude 1.3 or GPT-4 Vision Preview?
GPT-4 Vision Preview supports 128K tokens, while Claude 1.3 supports 100K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Claude 1.3 or GPT-4 Vision Preview open source?
Claude 1.3 is listed under Unknown. GPT-4 Vision Preview is listed under Proprietary. 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, Claude 1.3 or GPT-4 Vision Preview?
GPT-4 Vision Preview 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.
Which is better for multimodal input, Claude 1.3 or GPT-4 Vision Preview?
GPT-4 Vision Preview 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.
Which is better for code execution, Claude 1.3 or GPT-4 Vision Preview?
GPT-4 Vision Preview has the clearer documented code execution signal in this comparison. If code execution is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Claude 1.3 and GPT-4 Vision Preview?
Claude 1.3 is available on the tracked providers still being sourced. GPT-4 Vision Preview is available on Azure OpenAI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.