$15.76K
1
9
$15.76K
1
9
Trader mode: Actionable analysis for identifying opportunities and edge
This market will resolve to the company that owns the model with the highest “Mathematics Average” score on the LiveBench AI model leaderboard (https://livebench.ai/#/), on March 31, 2026 at 12:00 PM ET. If two models are tied for the highest LiveBench Mathematics Average score at this market's check time, resolution will be based on whichever company's name, as it is described in this market group, comes first in alphabetical order. The primary source of resolution for this market will be Li
The prediction market currently prices a 46% chance that OpenAI will have the best AI model for coding on March 31, 2026, based on the LiveBench coding average leaderboard. This probability, trading at 46¢ on Polymarket, indicates the market views OpenAI as the slight frontrunner in a highly competitive field, but far from a certain winner. The remaining probability is distributed among eight other competitors, including Anthropic (trading around 20%), Google (around 12%), and xAI (around 10%). With just $47,000 in total volume, liquidity is thin, suggesting these odds are preliminary and sensitive to new information.
OpenAI’s leading position is primarily anchored by the established dominance of its models, like GPT-4 and the specialized Codex, in developer tools and benchmarks. The company has a proven track record of iterative model improvements and a massive research lead. However, the 46% price reflects significant uncertainty. Intense competition is a key factor, with Anthropic’s Claude and Google’s Gemini models demonstrating strong coding capabilities, and new entrants like xAI’s Grok rapidly advancing. The long 75-day resolution horizon means many research breakthroughs could occur.
Furthermore, the specific metric, the LiveBench “coding average,” is a dynamic and comprehensive benchmark designed to reduce overfitting. This makes past performance less predictive, as competitors can specifically optimize for this benchmark. The market is effectively weighing OpenAI’s incumbent advantage against the high volatility expected in a fast-moving, multi-billion-dollar R&D race.
The odds will be most volatile around major model releases and benchmark updates. A surprise launch of “GPT-5” or a similarly capable model from OpenAI before March 31 would likely cause its probability to surge. Conversely, a breakthrough release from a competitor like Anthropic’s next Claude iteration or a new version of Google’s Gemini Ultra could rapidly shift momentum. Official LiveBench leaderboard updates, which occur regularly, will serve as direct catalysts, moving prices each time a company gains or loses the top spot in the coding average. The thin liquidity will amplify price swings around these events.
This market is trading exclusively on Polymarket. The absence of a comparable market on Kalshi eliminates the possibility for cross-platform arbitrage analysis. This exclusivity, combined with the low liquidity, means the current 46% price for OpenAI should be interpreted as a tentative signal rather than a deeply liquid consensus. All price discovery is happening within this single, shallow pool of capital.
AI-generated analysis based on market data. Not financial advice.
This prediction market focuses on determining which company will possess the most advanced AI model for software coding as measured by the LiveBench 'coding average' score on March 31, 2026. The resolution is based on a specific, publicly verifiable benchmark from the LiveBench AI model leaderboard, a platform that provides standardized, rigorous evaluations of AI capabilities across multiple domains. The competition centers on the rapidly evolving field of code generation AI, where models are trained to understand, write, debug, and explain programming code, fundamentally changing how software is developed. This market is significant because it captures a critical arms race in enterprise AI, where superior coding assistants can dramatically boost developer productivity, reduce software development costs, and become a major competitive advantage for technology companies. Interest in this topic stems from the immense economic value at stake, with the AI in software development market projected to grow substantially, and from the technical prestige associated with leading this frontier of AI research. The outcome will signal which organization's research and engineering teams are currently leading in creating practical, high-performance AI tools for one of the most valuable professional applications of artificial intelligence.
The pursuit of AI that can write code dates back to the earliest days of computer science, but the modern era began in earnest with the rise of deep learning and large language models. A pivotal moment arrived in June 2021 when OpenAI, in collaboration with GitHub, launched GitHub Copilot, powered by the Codex model. This marked the first widespread, commercially available AI pair programmer, trained on billions of lines of public code. It demonstrated that transformer-based models, pre-trained on natural language and fine-tuned on code, could provide meaningful, context-aware code suggestions directly within a developer's integrated development environment (IDE). This breakthrough triggered an industry-wide race. In 2023, the landscape expanded with the release of specialized models like Meta's Code Llama and continued enhancements to general-purpose models like GPT-4 and Claude, which showed improved coding prowess. The establishment of standardized benchmarks like LiveBench and its predecessor, HumanEval (introduced by OpenAI in 2021), provided a crucial framework for objectively comparing these models' abilities on tasks such as code completion, bug fixing, and problem-solving. The historical trajectory shows a rapid evolution from research prototypes to essential productivity tools, with benchmark scores serving as the primary metric for tracking technical progress in this high-stakes domain.
The competition to build the best AI coding model has profound implications for the global software industry and the broader economy. Superior AI assistants can dramatically increase developer productivity, potentially alleviating chronic shortages of skilled software engineers and accelerating the pace of digital innovation across all sectors, from finance to healthcare. The company that leads this field stands to capture immense economic value through direct product sales, platform lock-in, and the attraction of top developer talent to its ecosystem. Beyond economics, the outcome influences the strategic balance of power in the technology sector. A model's coding capability is often a strong proxy for its general reasoning and problem-solving skills, suggesting that leadership in this area could translate to advantages in other complex AI applications. Furthermore, the choice between open-source leaders like Meta and proprietary leaders like OpenAI will shape the accessibility, cost, and security of these powerful tools, affecting millions of developers and the long-term structure of the software development industry.
As of late 2024 and early 2025, the race is intensely competitive and dynamic. OpenAI continues to refine its models, Anthropic has released new versions of Claude with improved coding, and Google's Gemini models are being actively evaluated. Meta's open-source strategy with Code Llama has gained significant community traction. The LiveBench leaderboard is updated regularly with new model submissions, showing frequent changes in rankings. All major players are investing heavily in research, with a focus not just on raw benchmark scores but also on factors like reasoning latency, context window length, and integration into full developer toolchains. The path to March 2026 will involve multiple new model releases from each contender.
LiveBench is an independent, continuously updated AI model evaluation platform. Its 'coding average' score is derived from a suite of standardized tests that assess a model's proficiency at tasks like code generation from natural language descriptions, debugging existing code, and answering technical programming questions, providing a composite measure of coding intelligence.
These models are primarily trained using a two-stage process. First, they are pre-trained on a massive corpus of publicly available text and code from the internet, learning statistical patterns and syntax. Then, they are often fine-tuned on high-quality datasets of code-specific tasks and sometimes further refined with reinforcement learning from human or AI feedback to improve correctness and efficiency.
General models like GPT-4 are trained on a wide variety of text and can perform coding tasks among many others. Specialized coding models like Code Llama are fine-tuned extensively on code datasets, which can make them more efficient and accurate for programming-specific queries, though they may be less capable in broad conversation.
Current consensus is that these models act as powerful assistants or 'pair programmers,' automating repetitive tasks, suggesting boilerplate code, and helping debug, thereby increasing productivity. They are not seen as replacing the need for human engineers who provide critical problem-solving, architectural design, and business context understanding.
Key limitations include the potential to generate plausible-looking but incorrect or insecure code (hallucinations), difficulties with very complex or novel problems requiring deep reasoning, and a lack of true understanding of broader system architecture or specific business logic requirements not present in the training data.
Educational content is AI-generated and sourced from Wikipedia. It should not be considered financial advice.
Share your predictions and analysis with other traders. Coming soon!
9 markets tracked
No data available
| Market | Platform | Price |
|---|---|---|
| Poly | 44% |
 | Poly | 21% |
 | Poly | 20% |
 | Poly | 14% |
 | Poly | 5% |
 | Poly | 0% |
 | Poly | 0% |
 | Poly | 0% |
 | Poly | 0% |
No related news found
Add this market to your website
<iframe src="https://predictpedia.com/embed/JOUVNg" width="400" height="160" frameborder="0" style="border-radius: 8px; max-width: 100%;" title="Which company will have the best AI model for math on March 31?"></iframe>