GPT-5.3-Codex vs Perceptron Mk1
GPT-5.3-Codex (2026) and Perceptron Mk1 (2026) are agentic coding models from OpenAI and Perceptron. GPT-5.3-Codex ships a 400K-token context window, while Perceptron Mk1 ships a 33K-token context window. On pricing, Perceptron Mk1 costs $0.15/1M input tokens versus $1.75/1M 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.
Perceptron Mk1 is ~1067% cheaper at $0.15/1M; pay for GPT-5.3-Codex only for coding workflow support.
Decision scorecard
Local evidence first| Signal | GPT-5.3-Codex | Perceptron Mk1 |
|---|---|---|
| Decision fit | Coding, RAG, and Agents | Vision and JSON / Tool use |
| Context window | 400K | 33K |
| Cheapest output | $14/1M tokens | $1.5/1M tokens |
| Provider routes | 2 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- GPT-5.3-Codex has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GPT-5.3-Codex has broader tracked provider coverage for fallback and procurement flexibility.
- GPT-5.3-Codex uniquely exposes Function calling, Tool use, and Code execution in local model data.
- Local decision data tags GPT-5.3-Codex for Coding, RAG, and Agents.
- Perceptron Mk1 has the lower cheapest tracked output price at $1.5/1M tokens.
- Perceptron Mk1 uniquely exposes Multimodal in local model data.
- Local decision data tags Perceptron Mk1 for Vision and JSON / Tool use.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
GPT-5.3-Codex
$4,900
Cheapest tracked route: OpenRouter
Perceptron Mk1
$495
Cheapest tracked route: OpenRouter
Estimated monthly gap: $4,405. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- Perceptron Mk1 is $12.50/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Function calling, Tool use, and Code execution before moving production traffic.
- Perceptron Mk1 adds Multimodal in local capability data.
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- GPT-5.3-Codex is $12.50/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Multimodal before moving production traffic.
- GPT-5.3-Codex adds Function calling, Tool use, and Code execution in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-02-05 | 2026-05-12 |
| Context window | 400K | 33K |
| Parameters | — | — |
| Architecture | decoder only | decoder only |
| License | Proprietary | Proprietary |
| Knowledge cutoff | 2025-08 | - |
Pricing and availability
| Pricing attribute | GPT-5.3-Codex | Perceptron Mk1 |
|---|---|---|
| Input price | $1.75/1M tokens | $0.15/1M tokens |
| Output price | $14/1M tokens | $1.5/1M tokens |
| Providers |
Capabilities
| Capability | GPT-5.3-Codex | Perceptron Mk1 |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | No | Yes |
| Reasoning | Yes | Yes |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | Yes |
| Code execution | Yes | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on multimodal input: Perceptron Mk1, function calling: GPT-5.3-Codex, tool use: GPT-5.3-Codex, and code execution: GPT-5.3-Codex. Both models share vision, reasoning mode, and 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-5.3-Codex lists $1.75/1M input and $14/1M output tokens, while Perceptron Mk1 lists $0.15/1M input and $1.5/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Perceptron Mk1 lower by about $4.87 per million blended tokens. Availability is 2 providers versus 1, so concentration risk also matters.
Choose GPT-5.3-Codex when coding workflow support, larger context windows, and broader provider choice are central to the workload. Choose Perceptron Mk1 when vision-heavy evaluation 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-5.3-Codex or Perceptron Mk1?
GPT-5.3-Codex supports 400K tokens, while Perceptron Mk1 supports 33K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is cheaper, GPT-5.3-Codex or Perceptron Mk1?
Perceptron Mk1 is cheaper on tracked token pricing. GPT-5.3-Codex costs $1.75/1M input and $14/1M output tokens. Perceptron Mk1 costs $0.15/1M input and $1.5/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GPT-5.3-Codex or Perceptron Mk1 open source?
GPT-5.3-Codex is listed under Proprietary. Perceptron Mk1 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, GPT-5.3-Codex or Perceptron Mk1?
Both GPT-5.3-Codex and Perceptron Mk1 expose vision. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, GPT-5.3-Codex or Perceptron Mk1?
Perceptron Mk1 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-5.3-Codex and Perceptron Mk1?
GPT-5.3-Codex is available on OpenRouter and OpenAI API. Perceptron Mk1 is available on OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Continue comparing
Last reviewed: 2026-05-18. Data sourced from public model cards and provider documentation.