Prioritizing Large Language Models for Scientific Research and Agentic AI: A LiveBench-Grounded Audit (August 2026)

In the paper's words

An audit of the frontier large-language-model landscape (August 2026) for mathematics-heavy scientific research and agentic AI, grounded in the LiveBench 2026-06-25 contamination-free benchmark. Findings: no Llama model ranks in the top 42; DeepSeek V4 Pro 0813 is the mathematics price-performance optimum (95.1 at USD 0.044/run); Qwen 3.8 Max, Kimi K3 and Smaug-Agentic lead open-weights agentic capability; a five-tier prioritization is defined and deployed to the production inference router (v4.4.0). All numeric claims verified against live sources by an independent reviewer.

The abstract, verbatim, and its opening sentence is the claim. Every line below is quoted from the text.

Numbers it reports

1. The cost advantage is 6-8x against those models.

   from Finding 2: DeepSeek V4 Pro 0813 is the mathematics price-performance optimum

2. Selected over higher-aggregate open models because the program's discriminator is mathematics (gap of 3.8-11.2 points over Qwen/Kimi/Smaug) and the cost gap is 6-8x.

   from Prioritization Framework

3. Claude 5 Opus Thinking (Mathematics 95.7, Agentic 65.2), GPT-5.6 Sol (96.2), GPT-5.6 Terra Max (94.9 at USD 0.352 — 32 percent cheaper than Sol for near-equal mathematics), Grok 4.6 xHigh (93.1 at USD 0.228), Gemini 3.1 Pro (91.0).

   from Prioritization Framework

4. This audit was commissioned to (a) determine which models are suitable for the mathematics-heavy discourse of the QNFO research program, (b) identify suitable models for agentic multi-agent orchestration, (c) exclude models whose training corpus or empirical performance is inadequate for advanced scientific discourse, and (d) map the selected models onto the existing Cloudflare-based inference infrastructure (AI Gateway, Workers AI, DeepSeek API) within a self-funded budget constraint of USD 100 per month for Cloudflare billing.

   from Introduction

5. Table 1 reports the top-ranked models on LiveBench 2026-06-25, restricted to models relevant to this audit (top 15 by Overall plus open-weights and cost-effective candidates).

   from Leaderboard overview

6. LiveBench 2026-06-25: selected models, overall/mathematics/agentic scores and cost per benchmark run.

   from Leaderboard overview

How it is done

1. Scientific research programs that rely on large language models (LLMs) for mathematics-heavy derivation, formal proof verification, literature synthesis, and agentic tool orchestration face a model-selection problem that general-purpose leaderboards do not directly answer.

   from Abstract

2. Models were scored against the program's actual workload profile: Mathematics (primary discriminator): p-adic/ultrametric analysis, Laws of Form formal algebra, measure-theoretic stratigraphy, proof verification.

   from Evaluation criteria

3. The LiveBench 2026-06-25 leaderboard contains 42 ranked entries.

   from Finding 1: No Llama model ranks in the top 42

4. Reproducibility: All scores are verifiable against the cited live sources; the extraction and verification protocol is described in Methods.

   from Declarations

What would falsify it

The paper's text states no explicit falsification criterion; the limits below are what it says about where the claim stops.

Where the claim stops

1. A re-audit is scheduled for the next LiveBench release (approximately December 2026).

   from Conclusion

2. Scientific research programs that rely on large language models (LLMs) for mathematics-heavy derivation, formal proof verification, literature synthesis, and agentic tool orchestration face a model-selection problem that general-purpose leaderboards do not directly answer.

   from Abstract

3. Table 1 reports the top-ranked models on LiveBench 2026-06-25, restricted to models relevant to this audit (top 15 by Overall plus open-weights and cost-effective candidates).

   from Leaderboard overview

Where to read

1. Abstract236 words · 1 min
2. Introduction147 words · 1 min
3. Methods303 words · 1 min
4. Results502 words · 2 min
5. Prioritization Framework242 words · 1 min
6. Deployment85 words · 1 min
7. Discussion109 words · 1 min
8. Conclusion86 words · 1 min
9. Declarations117 words · 1 min