Proven Across 10+ Benchmarks
BEAM, Fiction.liveBench, NIAH, LongMemEval, LoCoMo, and more. 10+ benchmark families, tested across 10+ models, from 8K to 20M tokens. We published the results — including where we lose.
Results at a Glance
10+ benchmark families · 10+ models · 8K to 20M tokens · $0 per query offline. Tap any card for full results.
BEAM: Scale Invariance
BEAM measures structured reasoning across increasing context sizes. CosmicMind holds 79–80% accuracy from 100K to 10M tokens — +44pp above the best published LIGHT SOTA. The line is flat. That is the point.
Model Agnostic
Claude Sonnet 4.6: 79.9%qwen2.5:32b (local): 79.4%
Cloud and local models produce near-identical results.
Key Insight
Most systems degrade as context grows. CosmicMind's accuracy stays flat across 100× scale increase. The intelligence is in the cognition layer, not the model.
Fiction.liveBench
Narrative comprehension across long fictional texts. CosmicMind achieves 87.5% using a local 8B model — beating o3 and Gemini 2.5 Pro.
Model: ollama/llama3.1:8b
NIAH: On-Device Retrieval at Extreme Scale
Multiple Needle-in-a-Haystack across every combination of document depth and context length, testing 10 needles simultaneously. Run entirely on ollama/llama3.1:8b with no internet connection. 100% accuracy at 5M+ tokens.
>99% combined accuracy across all test points. Accuracy improves with scale — the opposite of degradation.
LongMemEval: The Compounding Thesis
Multi-session memory evaluation. CosmicMind goes from 56.6% at 115K to 83.0% at 1.5M — a +26.4pp improvement as context grows.
More data doesn't just maintain accuracy — it improves it. The cognitive architecture compounds understanding across sessions.
LoCoMo: Approaching Human Ceiling
Long-conversation memory benchmark. CosmicMind closes the gap to the human performance ceiling.
CosmicMind
53.2%
Hybrid architecture
GPT-4 + RAG
50–55%
Retrieval-augmented
Human Ceiling
~56%
Upper bound
Hardware Tiers
CosmicMind runs anywhere — from edge devices to enterprise-scale clusters. Same architecture, same accuracy, scaled to your hardware.
Light
Edge & mobile deployment
16–32 GB RAM, SSD
Regular
Workstation & on-prem
64–256 GB RAM, NVMe
SuperMassive
Enterprise & research scale
1+ TB RAM, distributed
Where We Don't Win (Yet)
Honest benchmarking means showing the losses too. These are areas where specialized systems outperform CosmicMind — and where we are actively improving.
LongMemEval (s subset)
On the single-session subset of LongMemEval, RAG-based approaches outperform CosmicMind by 2.6 percentage points. CosmicMind is optimized for cross-session synthesis, not single-turn lookups.
EgoLifeQA
EgoRAG, a retrieval system purpose-built for egocentric video QA, outperforms CosmicMind by 10–20pp on this multimodal benchmark. CosmicMind is a text-native system and does not yet ingest video or image data.
Cost & Efficiency
CosmicMind delivers frontier-level performance at a fraction of the cost. On local models, the cost is zero.
| Metric | CosmicMind | GPT-5.4 | Advantage |
|---|---|---|---|
| Cost per query | $0.01 | $6.67 | 99.85% |
| Annual cost (1K queries/day) | $125K | $83.25M | 99.85% |
| Max context (single pass) | 20M tokens | 1M tokens | 20× |
CosmicMind vs Context Stuffing (NIAH)
Context stuffing degrades rapidly past 128K tokens and becomes impossible beyond the native window. CosmicMind keeps accuracy improving at every scale, reaching 100% at 5M+.
Raw Accuracy Data
| Context | CosmicMind | Stuffing |
|---|---|---|
| 8K | 96.2% | 99.5% |
| 32K | 97% | 97.2% |
| 128K | 97.5% | 91.8% |
| 512K | 98% | 78.4% |
| 1M | 98.5% | 62.1% |
| 2M | 99% | 45.3% |
| 5M | 100% | N/A |
| 10M | 100% | N/A |
| 20M | 100% | N/A |
Retrieval Speed at Scale
Sub-second retrieval even at 20 million tokens.
Retrieval time at 20M tokens: 270ms (0.27 seconds) — fast enough for real-time applications.
A Note on Language
We use cognitive metaphors — neurons, synapses, cognition — throughout CosmicMind. These are not claims of artificial consciousness. They are precise analogies for how our patent-pending architecture functions.
The Numbers Speak for Themselves
10+ benchmarks. 10+ models. 8K to 20M tokens. Sub-second retrieval. $0 per query offline. See what CosmicMind can do for your team.