LLM Reference

GPT-4 vs Mistral 7B v0.1

GPT-4 (2023) and Mistral 7B v0.1 (2023) are compact production models from OpenAI and MistralAI. GPT-4 ships a 8K-token context window, while Mistral 7B v0.1 ships a 8K-token context window. On pricing, Mistral 7B v0.1 costs $0.05/1M 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.

Mistral 7B v0.1 is ~59900% cheaper at $0.05/1M; pay for GPT-4 only for coding workflow support.

Decision scorecard

Local evidence first
SignalGPT-4Mistral 7B v0.1
Decision fitCoding, Agents, and VisionGeneral
Context window8K8K
Cheapest output$60/1M tokens$0.15/1M tokens
Provider routes4 tracked16 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose GPT-4 when...
  • 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 Mistral 7B v0.1 when...
  • Mistral 7B v0.1 has the lower cheapest tracked output price at $0.15/1M tokens.
  • Mistral 7B v0.1 has broader tracked provider coverage for fallback and procurement flexibility.

Monthly cost at traffic

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

Lower estimate Mistral 7B v0.1

GPT-4

$39,000

Cheapest tracked route: OpenAI API

Mistral 7B v0.1

$77.50

Cheapest tracked route: DeepInfra

Estimated monthly gap: $38,923. Batch, cache, and negotiated pricing are excluded from this local estimate.

Switch friction

GPT-4 -> Mistral 7B v0.1
  • No overlapping tracked provider route is sourced for GPT-4 and Mistral 7B v0.1; plan for SDK, billing, or endpoint changes.
  • Mistral 7B v0.1 is $59.85/1M 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.
Mistral 7B v0.1 -> GPT-4
  • No overlapping tracked provider route is sourced for Mistral 7B v0.1 and GPT-4; plan for SDK, billing, or endpoint changes.
  • GPT-4 is $59.85/1M 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
Released2023-03-142023-09-27
Context window8K8K
Parameters1.76T (8x222B MoE)*7B
Architecturemixture of expertsdecoder only
LicenseProprietaryApache 2.0
Knowledge cutoff2021-092023-12

Pricing and availability

Pricing attributeGPT-4Mistral 7B v0.1
Input price$30/1M tokens$0.05/1M tokens
Output price$60/1M tokens$0.15/1M tokens
Providers

Capabilities

CapabilityGPT-4Mistral 7B v0.1
VisionYesNo
MultimodalYesNo
ReasoningNoNo
Function callingYesNo
Tool useNoNo
Structured outputsYesNo
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, structured outputs: GPT-4, and code execution: GPT-4. 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.

For cost, GPT-4 lists $30/1M input and $60/1M output tokens, while Mistral 7B v0.1 lists $0.05/1M input and $0.15/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mistral 7B v0.1 lower by about $38.92 per million blended tokens. Availability is 4 providers versus 16, so concentration risk also matters.

Choose GPT-4 when coding workflow support are central to the workload. Choose Mistral 7B v0.1 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. 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 Mistral 7B v0.1?

GPT-4 supports 8K tokens, while Mistral 7B v0.1 supports 8K 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 Mistral 7B v0.1?

Mistral 7B v0.1 is cheaper on tracked token pricing. GPT-4 costs $30/1M input and $60/1M output tokens. Mistral 7B v0.1 costs $0.05/1M input and $0.15/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is GPT-4 or Mistral 7B v0.1 open source?

GPT-4 is listed under Proprietary. Mistral 7B v0.1 is listed under Apache 2.0. 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 Mistral 7B v0.1?

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 Mistral 7B v0.1?

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 Mistral 7B v0.1?

GPT-4 is available on OpenAI API, Azure OpenAI, Salesforce Einstein Generative AI, and OpenRouter. Mistral 7B v0.1 is available on GCP Vertex AI, OctoAI API (Deprecated), DeepInfra, Mistral AI Studio, and Baseten API. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

Continue comparing

Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.