GPT-5.4-Cyber vs RWKV-7 Goose 2.9B
GPT-5.4-Cyber (2026) and RWKV-7 Goose 2.9B (2025) are frontier reasoning models from OpenAI and RWKV Project. GPT-5.4-Cyber ships a not-yet-sourced context window, while RWKV-7 Goose 2.9B ships a Infinite-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-5.4-Cyber is safer overall; choose RWKV-7 Goose 2.9B when provider fit matters.
Decision scorecard
Local evidence first| Signal | GPT-5.4-Cyber | RWKV-7 Goose 2.9B |
|---|---|---|
| Best for | reasoning-heavy apps and multimodal apps | general production evaluation |
| Decision fit | Vision | Long context |
| Context window | — | Infinite |
| Cheapest output | - | - |
| Provider routes | 0 tracked | 0 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- GPT-5.4-Cyber uniquely exposes Multimodal and Reasoning in local model data.
- Local decision data tags GPT-5.4-Cyber for Vision.
- RWKV-7 Goose 2.9B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags RWKV-7 Goose 2.9B for Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
GPT-5.4-Cyber
Unavailable
No complete token price in local provider data
RWKV-7 Goose 2.9B
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for GPT-5.4-Cyber and RWKV-7 Goose 2.9B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Multimodal and Reasoning before moving production traffic.
- No overlapping tracked provider route is sourced for RWKV-7 Goose 2.9B and GPT-5.4-Cyber; plan for SDK, billing, or endpoint changes.
- GPT-5.4-Cyber adds Multimodal and Reasoning in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-04-14 | 2025-03-18 |
| Context window | — | Infinite |
| Parameters | — | 2.9B |
| Architecture | decoder only | decoder only |
| License | Proprietary | Apache 2.0 |
| Knowledge cutoff | 2025-08 | - |
Pricing and availability
| Pricing attribute | GPT-5.4-Cyber | RWKV-7 Goose 2.9B |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers | - | - |
Pricing not yet sourced for either model.
Capabilities
| Capability | GPT-5.4-Cyber | RWKV-7 Goose 2.9B |
|---|---|---|
| Vision | No | No |
| Multimodal | Yes | No |
| Reasoning | Yes | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | No |
| Code execution | No | No |
| 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 multimodal input: GPT-5.4-Cyber and reasoning mode: GPT-5.4-Cyber. 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: GPT-5.4-Cyber has no token price sourced yet and RWKV-7 Goose 2.9B has no token price sourced yet. Provider availability is 0 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose GPT-5.4-Cyber when reasoning depth are central to the workload. Choose RWKV-7 Goose 2.9B when provider fit 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. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Is GPT-5.4-Cyber or RWKV-7 Goose 2.9B open source?
GPT-5.4-Cyber is listed under Proprietary. RWKV-7 Goose 2.9B 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 multimodal input, GPT-5.4-Cyber or RWKV-7 Goose 2.9B?
GPT-5.4-Cyber 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 reasoning mode, GPT-5.4-Cyber or RWKV-7 Goose 2.9B?
GPT-5.4-Cyber has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
When should I pick GPT-5.4-Cyber over RWKV-7 Goose 2.9B?
GPT-5.4-Cyber is safer overall; choose RWKV-7 Goose 2.9B when provider fit matters. If your workload also depends on reasoning depth, start with GPT-5.4-Cyber; if it depends on provider fit, run the same evaluation with RWKV-7 Goose 2.9B.
Continue comparing
Last reviewed: 2026-05-25. Data sourced from public model cards and provider documentation.