Model Detail
Fireworks: Ember-1
—Fireworks: Ember-1 is a multimodal model released by Fireworks. And supports text+image->text inputs.
Fireworks: Ember-1 is priced at $3/M input tokens and $15/M output tokens. Operationally the model offers a 1049K-token context window, which matters when sizing it for prompt-heavy or latency-sensitive workloads. Pricing in this range is the working middle of the API market — neither the cheapest nor the most expensive option per token, so cost-fit is usually a function of how much output you generate.
Fireworks: Ember-1 is published on Hugging Face but our pipeline has not yet captured architecture, license, or parameter-count metadata for this entry. The data is refreshed daily, so these fields typically populate within 24–48 hours of release.
Fireworks: Ember-1 is best fit for mixed text-and-image reasoning tasks such as document understanding, and long-context tasks such as full-codebase analysis or book-length summarization (1049K tokens). Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
EMBER: Predicting TCR--pMHC Binding Without a Docked Complex
arXiv:2610.27334v1 Announce Type: cross Abstract: We propose EMBER, a structure-based framework for TCR--pMHC binding prediction that eliminates the need for a predicted docked complex, and with it the placement error that dominates such predictions. Traditional structure-based predictors utilize th
Ember: A Kilobyte-State Optimizer for Token Interfaces
arXiv:2607.01455v4 Announce Type: replace-cross Abstract: Language models learn continuous programs over discrete symbols, with the embedding table and LM-head acting as the read/write interface between them. We show that this interface has gradient geometry distinct from dense hidden weights which
EMBER: Efficient Memory via Budgeted Evidence Retention for Long-Horizon Agents
arXiv:2606.05894v1 Announce Type: new Abstract: Long-horizon agents can archive large histories, but future answers still incur retrieval, rereading, and context costs. When retained memory misses answer-relevant evidence, the system must return to larger portions of the raw history. We study budget
