Fortytwo has launched Engine, an inference API that coordinates multiple AI models during generation. The company reports that its initial Swarm Decoder setup, using Qwen 3.8 27B, used 24.7% fewer tokens than the same model running alone across 450 DeepSWE software-engineering tasks, while raising the benchmark score from 42.2 to 47.8.
That result is a company-reported benchmark finding, not a guaranteed saving for every model, prompt or production workload. Fortytwo has not published independent replication or customer production measurements in the available launch coverage.
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What Fortytwo Engine does
Engine is a cooperative inference layer for open-weight models. Instead of asking one model to produce a complete answer in isolation, Fortytwo’s Swarm Decoder lets cooperating model instances rank and challenge reasoning while a response is still being generated. The goal is to identify weak assumptions before they trigger additional generation, retries or tool calls.
This differs from Fortytwo’s earlier 2025 swarm approach, which generated separate answers, compared them after completion and selected stronger contributions. Engine moves peer evaluation into the shared reasoning trajectory. Fortytwo says its token accounting includes every participating model, rather than counting only the final response.
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What the 24.7% result means
Fortytwo’s headline figure comes from 450 DeepSWE runs. Engine averaged 12.3 million tokens per run, compared with 16.3 million for the base Qwen 3.8 27B model, and achieved a DeepSWE score of 47.8 versus 42.2.
| Measure | Qwen 3.8 27B alone | Qwen 3.8 27B with Engine |
|---|---|---|
| DeepSWE score | 42.2 | 47.8 |
| Average tokens per run | 16.3 million | 12.3 million |
| Total tokens across 450 runs | 7.4 billion | 5.6 billion |
| Reported token reduction | 24.7% for these DeepSWE runs | |
The reported percentage applies specifically to this evaluation. It should not be read as a fixed reduction in API bills, latency or tokens for other benchmarks, models, prompts or tool-using applications. The published figures are company-reported.
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Other benchmark results reported by Fortytwo
Fortytwo’s launch materials list these comparisons for Qwen 3.8 27B under its evaluation protocol:
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| Benchmark | Base model | Engine |
|---|---|---|
| DeepSWE | 42.2 | 47.8 |
| Humanity’s Last Exam | 34.7 | 35.8 |
| GDP.pdf | 18.5 | 20.6 |
| GPQA-Diamond | 90.9 | 95.1 |
| SciCode | 46.8 | 49.0 |
| LiveCodeBench v6 | 80.2 | 84.1 |
These numbers come from Fortytwo’s own evaluation and have not been independently verified in the material available for this launch. They show higher reported scores across the listed tests, but do not establish how Engine performs on a particular organization’s workload.
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Is Engine compatible with the OpenAI API?
Fortytwo describes Engine as compatible with the OpenAI Chat Completions API. Documented capabilities include:
- Streaming responses
- Function tools
- Parallel tool calls
- Structured output
- A reasoning-effort setting
- Chat Completions requests and model listing
Compatibility has clear boundaries. Responses, Embeddings, Audio, Files and Assistants are listed as unsupported. Some request fields are only partially supported or depend on the selected model, and unsupported fields return errors. A migration should therefore begin with an audit of the exact endpoints, parameters and response handling used by an application rather than a simple base-URL replacement.
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Migration checks for developers
- Inventory every OpenAI endpoint your application calls and confirm that it is a supported Chat Completions operation.
- Test streaming, structured-output schemas, function definitions and parallel tool calls with representative requests.
- Check model-list responses and select the supported Qwen 3.8 27B option explicitly.
- Exercise error handling for unsupported or model-dependent fields before switching production traffic.
- Measure completed-task tokens, tool-call retries, latency and success rate on your own prompts; the DeepSWE reduction is not a forecast for those results.
Can organizations run the swarm privately?
Fortytwo Harbor is the company’s private-deployment option. It is positioned for organizations that want swarm inference on their own infrastructure, keeping data inside the organization’s perimeter and replacing per-token API billing with fixed hardware costs.
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Why the launch matters—and what remains unknown
Engine’s proposed advantage is unusual: coordinating models is intended to improve task quality while reducing total generation. Fortytwo CEO and co-founder Ivan Nikitin summarized the claim as: “Collective reasoning pays off twice: the answers are better, and tasks take fewer tokens.”
Fortytwo was founded in 2024. Unite.AI reports that it has 15 engineers in Mountain View and Abu Dhabi, raised $2.3 million in pre-seed funding in 2025 and joined Abu Dhabi’s Hub71 ecosystem in 2026. The company says it plans to add more open models to Engine after Qwen 3.8 27B. Those company details and roadmap plans can change.
For an engineering team considering adoption, the central unanswered question is transferability. The available evidence does not include independent benchmark replication, customer production data, or a latency study. Engine is best treated as a promising inference architecture with a strong reported DeepSWE result, followed by workload-specific testing before any cost or quality commitment.
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