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  • Similar feature launched open-source and end-to-end encrypted on my TrustedRouter https://trustedrouter.com/
  • Spent the weekend inspired by the new openrouter fusion model and wanted to see if it could run in Claude Code and if I could make it very easy for everyone else to try.

    Built - claude-fusion-launcher — run Claude Code on a panel of models, not just one

    Also shows cost

    https://github.com/smorinlabs/claude-fusion-launcher

  • Some anecdata on Fusion: I run same query I used for Fable on OR Fusion and results were worse.

    It felt, like Fable was able to kinda grasp very deep knowledge/intelligence layers and outline solution not only in agreeable way, but rather it proposed to prioritize solution items, with discarding some of the items, which made a lot of sense to me.

    While Fusion felt more like a bit diversified answer of the same class of pre-Fable SOTA models, without touching the depth of knowledge/intelligence layers, which Fable was able to get, in my very limited tests I did, while Fable was accessible.

  • I have been thinking a lot about this and my simplified understanding is that each model can be seen as a bell curve over human knowledge and each model has a different distribution. Using multiple models would allow us to change the distribution of other models with text that is out of their original curve. But then if you think about it does SFP and RL even alter the original distribution of text enough that models have enough variety so that their combined output is something better or just an echo chamber I believe not but I have no way to prove it yet.
  • I ran a quick eval to see what this looks like qualitatively vs just calling Opus 4.7 or GPT 5.5 directly.

    As expected, Fusion was 7x slower and 4x the cost.

    This isn't a knock against it, just that it I think this places Fusion into a "use it only when you need it" category.

    https://3fpi5avcqq.evvl.io/

  • Context:

    Surpassing Frontier Performance with Fusion

    https://news.ycombinator.com/item?id=48525392

    And a slightly better UI here: https://openrouter.ai/fusion

    On OpenRouter's fusion API your request is routed to several models simultaneously and a judge model combines their answers into a final response. This significantly boosts performance, at the cost of time (at least on the one benchmark they tested, a deep research benchmark).

    They have a Budget preset consisting of 3 cheaper models (which roughly matches Fable on that benchmark, costing half as much), and a Quality preset of 3 expensive ones (which beats Fable, but costs twice as much as Fable).

    Pareto graph: https://openrouter.ai/blog/images/blog/fusion-benchmark-cost...

    Curiously, fusing a model with itself also boosted performance (2xOpus4.8 roughly matching Fable on the benchmark, but costing twice as much as Fable). There's a further, smaller gain from mixing different models. The main gain seems to be from additional test time compute.

    Would love to see more research on this, especially focusing on the cheap models that came out recently (e.g. Fusing DSV4 with itself, or with Mimo), and to see what the tradeoffs look like between running a fusion (parallel test time compute) vs increased reasoning or turns.

  • I had a prompt I used for this just using Claude Code:

        Let's review <filepath or specific file> for architectural issues. Spawn 10 agents, create personas for them, have them review the _api.go and write their review to reviews/<persona>-review.md, then have each agent do a round robin response to 3 of the reviews of their choosing (based on the abstract at the beginning of each review) and write the response to response/<original file name>-<agent persona name>-response.md. Then we do rebuttals to the responses in rebuttals/<response file name>-rebuttal.md. Finally, each agent should launch agents to review the reviews, responses, and rebuttals to their review, and compile findings to findings/<original file name>-findings.md. Finally, have another agent compile the findings and write that to review-findings.md. Present a concise version of the findings here.
    
    This works well with frontier models and even locally hosted models (last I used it was with Qwen 3.5).
    by all2
  • Heh. I built "Fusion" a few months ago as an MCP using OpenRouter. The idea was to give Claude a "panel of experts" to go talk to when it got stuck.

    After extensive testing and benchmarking I discovered that when you ask one model to judge another's response you don't actually get a better answer. You are just asking it "how closely does this resemble the answer you would have given me." Additional rounds and all the "obvious" solutions that pop into your mind reading the proceeding sentence are essentially just cranking up the temperature.

    I did find a solution, but it is insanely expensive. Maybe if this gains traction I'll release mine.

    by dsl

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