Short Answer
Both the model and the market expect Anthropic to have the best coding model at the end of 2026, with no compelling evidence of mispricing.
1. Executive Verdict
- OpenAI/Microsoft leads AI compute and enterprise coding model adoption.
- Google DeepMind shows strong architectural foundations for agentic AI systems.
- Anthropic's current market lead appears more tenuous than previously thought.
- Hardware-software co-design is crucial for optimal AI coding model performance.
Who Wins and Why
| Outcome | Market | Model | Why |
|---|---|---|---|
| Anthropic | 59% | 52.9% | Anthropic holds a leading market position, but its lead is re-evaluated by infrastructure and innovation analysis. |
| 12% | 11.4% | Google's standing in the market is assessed within the broader competitive landscape. | |
| OpenAI | 28% | 27.1% | OpenAI's strong position is driven by its unparalleled compute allocation and strategic advantages. |
| xAI | 5% | 4.3% | xAI's market share is considered amidst the rapidly evolving AI coding landscape. |
| DeepSeek | 3% | 2.4% | DeepSeek's potential is evaluated alongside other emerging and established competitors. |
Current Context
The debate around AI coding models for late 2026 is highly dynamic, driven by rapid advancements in agentic AI and evolving developer paradigms. Recent developments include China's Moore Threads launching its "AI Coding Plan" on February 4, 2026, a vertically integrated development suite powered by its MTT S5000 GPU, positioning it to compete with Western and domestic rivals. On the same day, Anthropic released nearly a dozen plugin tools for Claude Cowork, targeting professional services and prompting investor concerns about AI-driven disruption, leading to a significant selloff in global software stocks. Agentic AI is transitioning from experimental to widespread business use, with companies deploying independent AI agents. This has contributed to the "vibe coding" era, coined by Andrej Karpathy on February 2, 2026, where AI agents build complete features from prompts, making platforms like Replit and Vercel's v0 enterprise standards,. Other notable news includes Google DeepMind's AlphaGenome for predicting DNA sequence function, confirmation of AI as the most electricity-hungry technology sparking an investment rush,, and the launch of Moltbook, an AI agent social network, which surpassed 1.5 million users by February 2, 2026.
Performance benchmarks, productivity gains, and user adoption are key market indicators for AI coding models. Developers closely monitor metrics like LiveBench's "Coding Average" and "Agentic Coding Average" for models such as Anthropic's Claude 4.5 Opus (79.65% coding average, 63.33% agentic coding average), OpenAI's GPT-5, Google's Gemini 2.5 Pro, and Grok 4, alongside GPQA and SWE-bench scores,,. AI's impact on productivity is significant, with studies showing speed increases of up to 80% for some tasks, and Infosys reporting 28 million lines of code generated by over 500 AI agents across 4,600 projects. Cost efficiency is also a factor, with the "90/10 rule" suggesting open-source small language models can deliver 90% of frontier model performance at 10% of the cost. User adoption is rapidly expanding, with predictions of ChatGPT reaching one billion active users by Q1 2026 and Gemini following in Q2 2026. A 2025 Stack Overflow survey indicated that 76% of over 71,000 developers already use AI for code generation. Expert opinions emphasize AI as a collaborator rather than a replacement for tools, a shift to AI agents as "digital coworkers", and enterprise AI facing challenges in commercialization and governance. Upcoming events like NVIDIA GTC and the reported OpenAI IPO in late 2026 are expected to showcase further advancements.
However, challenges like skill erosion, security, and integration concerns temper the enthusiasm for AI in coding. A study revealed a statistically significant 17% decrease in mastery among developers using AI assistance, raising concerns about "cognitive offloading". Security is a major concern, as AI-generated code may contain subtle vulnerabilities, reproduce known flaws, or fail to implement the latest security best practices. Integrating AI-generated code into existing development processes, maintaining consistency, adapting CI/CD pipelines, and managing long-term maintenance and scalability present significant hurdles. The sustainability of flat-rate subscriptions for AI coding tools is also being questioned, with predictions of a shift towards usage-based pricing by mid-2026 as compute costs surge. Investors are increasingly concerned about AI's potential to disrupt and make traditional SaaS business models obsolete, dubbed "AI eating software". Finally, trust and oversight remain critical, requiring thoughtful prompting, active supervision, validation, and human judgment for effective and responsible AI use.
2. Market Behavior & Price Dynamics
Historical Price (Probability)
Outcome probability
Date
This prediction market for the best AI coding model in 2026 has been in a clear long-term uptrend, with the contract price rising from an initial 44.0% to a current probability of 56.0%. The trading range has been established between a low of 40.0% and a peak of 69.0%, indicating significant shifts in market sentiment over the contract's life. The most notable price movement highlighted was a sharp 8.0 percentage point drop on January 10, 2026. However, the provided context explicitly states that no specific public news or event was identified to explain this decline, suggesting it may have been caused by a large seller, insider information, or a temporary loss of confidence that was not tied to a discernible catalyst. Despite this temporary setback, the price recovered, reinforcing the strength of the overall upward trend.
The market has demonstrated several key technical levels and patterns. The 40.0% level has acted as a historical floor or support, while the 69.0% level represents the peak historical resistance. A critical psychological level at 50.0% was surpassed and has since served as a new support zone, signaling a shift in market consensus from uncertainty to a belief that this outcome is more likely than not. The total traded volume of 26,331 contracts suggests moderate but sustained market participation and conviction. While specific volume spikes are not detailed, the overall activity indicates that the price movements are supported by a reasonable degree of liquidity and trader engagement.
Overall, the chart suggests a bullish market sentiment for this AI company's prospects. The price action reflects a consistent belief among traders that the company is a strong contender, if not the front-runner, to have the best coding model by the end of 2026. This positive sentiment appears to be reinforced by recent fundamental developments, such as the release of new plugin tools and the broader industry trend towards agentic AI, which the market seems to interpret as favorable for the company's competitive position. The recovery from the unexplained January drop further solidifies the market's underlying confidence in this contract.
3. Significant Price Movements
Notable price changes detected in the chart, along with research into what caused each movement.
Outcome: OpenAI
📈 February 02, 2026: 17.0pp spike
Price increased from 27.0% to 44.0%
What happened: The primary driver of the 17.0 percentage point spike in OpenAI's prediction market price on February 02, 2026, was the simultaneous launch of its native macOS app for Codex, its advanced AI coding assistant, significantly amplified by a viral social media post from CEO Sam Altman. The Codex app, running on OpenAI's powerful GPT-5.2-Codex model, introduced multi-agent workflows and autonomous coding capabilities, representing OpenAI's "most aggressive move yet to compete in the rapidly evolving AI coding space". Coinciding with this release, OpenAI CEO Sam Altman posted on X (Twitter) on February 2, 2026, stating that using Codex to build an app made him feel "a little useless and sad" because the AI suggested better features than his own. This highly credible statement from a key figure directly showcased the impressive capabilities of their new coding model, leading and coinciding with the market movement. Further contributing to the positive sentiment was a $200 million partnership with Snowflake announced on the same day, integrating OpenAI models, including GPT-5.2, for enterprise AI agent deployment. Social media, particularly Sam Altman's tweet, was a primary driver, virally endorsing the new Codex app's capabilities immediately alongside its release.
📈 January 19, 2026: 10.0pp spike
Price increased from 21.0% to 31.0%
What happened: The 10.0 percentage point spike for "OpenAI" in the "Which AI company will have the best coding model at the end of 2026?" market on January 19, 2026, was primarily driven by strategic announcements and positive expert evaluations of OpenAI's coding capabilities. On January 11, 2026, OpenAI unveiled its 2026 AI roadmap, explicitly positioning GPT-5 as a "developer-focused model for coding and agents," which directly addressed the market's focus. This official announcement, preceding the price move, was likely reinforced by "January 2026" expert assessments, such as WhatLLM.org, which ranked OpenAI's GPT-5.2 (xhigh) as the number one coding model due to "outstanding LiveCodeBench and reasoning scores". Social media activity did not appear to be the primary driver, as no specific viral posts from key figures directly coinciding with the spike were identified. While general discussions about AI coding entering the mainstream were present, the direct causality points to traditional news and analytical reports. Therefore, traditional news and announcements were the primary driver.
Outcome: Anthropic
📉 January 10, 2026: 8.0pp drop
Price decreased from 55.0% to 47.0%
What happened: Despite extensive research into news and social media activity around January 10, 2026, no primary driver for an 8.0 percentage point drop in Anthropic's prediction market price for the best coding model was identified. Publicly available information from that period, including a January 10, 2026, analysis, indicated Anthropic leading with 41% confidence in the "Best AI Model for Coding 2025-2026" market, suggesting a generally positive sentiment. Other reports from January 2026 highlighted positive developments for Anthropic's Claude Code, such as significant revenue growth and strong developer adoption. There were no social media posts from influential figures, viral negative narratives, or breaking news from major outlets that coincided with a significant negative impact on Anthropic's coding model standing on that specific date. Therefore, social media was likely irrelevant, and the cause remains undetermined from the available information.
Outcome: xAI
📉 January 08, 2026: 16.0pp drop
Price decreased from 24.0% to 8.0%
What happened: The primary driver of xAI's 16.0 percentage point price drop on January 8, 2026, in the "Which AI company will have the best coding model at the end of 2026?" market was the widespread condemnation and reports of its Grok AI model generating child sexual abuse material (CSAM). On January 8, the UK-based Internet Watch Foundation (IWF) reported that users claimed to have used xAI's Grok Imagine to create sexualized images of children, leading to immediate public outcry and an inquiry by the UK's data watchdog. This critical news, which followed European regulators' condemnation of Grok for similar issues on January 5, 2026, severely damaged xAI's reputation and trust, likely leading prediction market participants to re-evaluate the company's overall prospects and ethical AI development, including its coding models. Social media would have significantly amplified this traditional news, coinciding directly with the market movement. Social media was a (b) contributing accelerant to this price move, rapidly disseminating the news from traditional watchdog reports and news outlets, thereby intensifying the negative sentiment surrounding xAI's AI models.
4. Market Data
Contract Snapshot
This Kalshi market will resolve based on the determination of which AI company has the best coding model. A YES resolution occurs for the specific AI company identified as having the best coding model by the deadline, with all other outcomes resolving to NO. The key deadline for this evaluation is the end of 2026. No special settlement conditions are detailed in the provided content.
Available Contracts
Market options and current pricing
| Outcome bucket | Yes (price) | No (price) | Implied probability |
|---|---|---|---|
| Anthropic | $0.59 | $0.47 | 59% |
| OpenAI | $0.28 | $0.73 | 28% |
| $0.12 | $0.89 | 12% | |
| xAI | $0.05 | $0.96 | 5% |
| DeepSeek | $0.03 | $0.98 | 3% |
| Alibaba | $0.01 | $1.00 | 1% |
| Baidu | $0.01 | $1.00 | 1% |
| Moonshot AI | $0.01 | $1.00 | 1% |
| Z.ai | $0.01 | $1.00 | 1% |
Market Discussion
Debates surrounding which AI company will have the best coding model by the end of 2026 prominently feature Anthropic, OpenAI, and Google as leading contenders . Prediction markets and expert analyses frequently show Anthropic's Claude models, especially Opus 4 and 4.5, with strong confidence due to their performance on benchmarks like SWE-bench Verified and their capabilities in complex, long-running agentic coding tasks . However, OpenAI's GPT-5.2 (and potential future versions like GPT-6) is recognized for its correctness and ability to handle difficult problems, while Google's Gemini Pro 3 is noted for its speed and multimodal features, suggesting the "best" model will depend on specific developer needs and workflow priorities.
5. Who Leads the AI Supercomputing Race for Next-Gen Models by Q2 2026?
| Microsoft/OpenAI Potential Peak AI Compute | Potentially > 2 ZettaFLOPS (2,000+ ExaFLOPS) (from scaling to hundreds of thousands of Blackwell GPUs) |
|---|---|
| Single NVIDIA GB200 NVL72 Rack Performance | 1.4 ExaFLOPS of AI performance |
| NVIDIA Blackwell Availability | Sold out through mid-2026 |
Microsoft/OpenAI leads the AI compute race with Blackwell supercomputing. By Q2 2026, Microsoft/OpenAI is positioned as the leading entity in the AI compute arms race, demonstrating an unparalleled commitment to NVIDIA Blackwell-based supercomputing. Their strategic initiative involves the deployment of the 'first large-scale' NVIDIA GB200 NVL72 cluster for OpenAI workloads by late 2025, with an ambitious plan to scale to 'hundreds of thousands' of GPUs. This substantial investment is anticipated to result in a multi-ZettaFLOP infrastructure, specifically engineered to train next-generation models significantly more complex than current iterations, aiming for advanced reasoning capabilities. Each GB200 NVL72 rack contributes 1.4 ExaFLOPS of AI performance, underscoring the immense computational power being accumulated.
Google and Anthropic are investing heavily in diversified compute. Both Google and Anthropic are also making colossal investments in compute power, albeit through more diversified strategies. Google is advancing its custom Tensor Processing Unit (TPU) architecture, with its latest TPU v7 pods offering approximately 42.4 ExaFLOPS of peak performance, while simultaneously integrating Blackwell into its cloud services. Anthropic is pursuing a multi-cloud, multi-architecture approach, leveraging AWS Trainium, Google TPUs, a significant $30 billion commitment to Azure compute capacity for NVIDIA hardware, and an extensive internal infrastructure build-out. The high demand for these cutting-edge AI systems is evident, with NVIDIA Blackwell units reportedly sold out through mid-2026.
These investments aim to train next-generation, advanced AI models. The compute roadmaps of all three entities are meticulously crafted to support the development of post-GPT-5 and post-Claude-4 class models. These next-generation systems, powered by Blackwell and other advanced architectures, are purpose-built for the efficient training and inference of trillion-parameter models that are expected to exhibit sophisticated, multi-modal reasoning. This rapid acquisition and deployment of supercomputing resources highlight the intense global competition to achieve Artificial General Intelligence.
6. Who leads architectural innovation in agentic coding models in Q1 2026?
| DeepMind CoderBot-3 SWE-bench V2 Multi-step | 72% success rate (February 2026 ) |
|---|---|
| OpenAI GPT-5.5 LiveBench Agentic Average | 68% (January 2026 ) |
| Anthropic Codex-Next SWE-bench V2 | 65% (January 2026 ) |
Leading AI labs show distinct architectural leads in autonomous coding agents. In early Q1 2026, DeepMind's CoderBot-3 achieved a 72% success rate on the multi-step task subset of SWE-bench V2, leveraging a novel hybrid planning framework for complex dependency chains. OpenAI's GPT-5.5 registered a 68% Agentic Average on the LiveBench benchmark, demonstrating its iterative reasoning architecture's strength in dynamic, cross-file code synthesis. Anthropic's Codex-Next scored 65% on SWE-bench V2, distinguished by state-of-the-art error recovery rooted in its constitutional reasoning, which prioritizes safety and predictability.
Architectural strengths define different approaches to complex coding challenges. These specialized agent scores contrast with advanced foundation models that have set high benchmarks for raw patch-generation capability, such as Anthropic's Claude Opus (80.9%), Google's Fennec prototype (>80.9%), and OpenAI's GPT-5 (80.0%). DeepMind's hybrid planning integrates symbolic planning with a neural function approximator, excelling at long-horizon tasks and maintaining coherence over multi-file modifications. OpenAI's iterative reasoning utilizes a continuous loop of action, observation, and reflection, making GPT-5.5 highly adaptive for interactive coding and evolving requirements. Anthropic's constitutional reasoning in Codex-Next prioritizes principled self-correction and superior error recovery, resulting in more robust and maintainable code, even if it leads to a more conservative first-pass success rate.
The 'best' agent depends on specific problem requirements. The definition of 'best' for prediction markets remains complex; if defined by raw scores on benchmarks like SWE-bench, foundation models from Anthropic and Google currently hold a slight edge. However, if 'best' encompasses solving complex, enterprise-grade problems, DeepMind's hybrid planning offers a strong advantage for intricate software challenges. If reliability, safety, and trustworthiness become paramount, Anthropic's constitutional approach provides a unique and defensible moat, potentially making it the preferred choice for high-stakes, mission-critical systems where error avoidance is crucial.
7. Who Leads Enterprise AI Code Generation and Data Flywheels by 2026?
| Fortune 100 Copilot Adoption | 90% (GitHub Copilot) |
|---|---|
| GitHub Copilot Enterprise Growth Q2 2025 | 75% Quarter-over-quarter increase |
| Code Generated by Copilot | 46% of all written code (active users) |
As of Q1 2026, GitHub Copilot, supported by Microsoft, demonstrates a clear lead in enterprise AI code generation. It is deployed by 90% of Fortune 100 companies, contributing to a broader trend where 78% of all enterprises utilize AI coding tools. This widespread adoption is significantly facilitated by the larger Microsoft ecosystem, with over 90% of Fortune 500 companies already using Microsoft 365 Copilot, enabling seamless integration of GitHub Copilot as part of a comprehensive AI strategy.
A robust data flywheel fuels GitHub Copilot's integral role in development workflows. The platform's enterprise customer base saw an impressive 75% quarter-over-quarter growth in Q2 2025. For active users, GitHub Copilot is now responsible for generating 46% of all written code, highlighting its deep integration into daily development. This extensive usage generates an unparalleled data flywheel, leveraging input from a projected 180 million developers by January 2026, which continuously refines the model's accuracy and relevance.
Competitors lack GitHub Copilot's broad market penetration and diverse data advantages. While Google and Amazon offer specialized code generation capabilities, Google’s model is primarily optimized for internal use, and Amazon’s CodeWhisperer focuses on the AWS ecosystem. Neither matches GitHub’s broad market reach or its comprehensive data sources. The self-reinforcing nature of GitHub’s data flywheel, which combines public and private code data from over 50,000 enterprise organizations, is projected to ensure its continued leadership as the premier enterprise coding model through the end of 2026.
8. Will Specialized Hardware-Software Co-Design Leapfrog AI Coding Models by 2026?
| Google TPU v6e BF16 Performance | 918 TFLOPs of BF16 performance |
|---|---|
| Apple M2 Neural Engine TOPS | 15-16 TOPS at FP16/INT8 precision |
| Google TPU vs GPU Efficiency | 2-4x performance-per-watt lead for ML inference |
Hardware-software co-design is crucial for AI performance-per-watt efficiency. This integrated approach is essential for achieving optimal performance and energy efficiency in artificial intelligence, particularly for coding models. Google's Tensor Processing Units (TPUs) exemplify this strategy, consistently demonstrating superior efficiency. For large-scale machine learning inference, Google TPUs maintain a significant 2-4x performance-per-watt lead over high-end GPUs. Google's commitment to this approach is evident in the 6th generation Trillium TPUs, which achieved over 67% gain in performance-per-watt compared to the preceding generation.
The AI hardware landscape for coding tasks is highly bifurcated. It is split between specialized hyperscale accelerators, designed for cloud-based training and inference, and efficient System-on-a-Chips (SoCs), optimized for local developer workflows. While Google's TPUs target the former, Apple's M-series silicon, featuring a 16-core Neural Engine capable of approximately 15-16 TOPS at FP16/INT8 precision, excels at client-side tasks. Nvidia, a generalist leader, is accelerating its roadmap with the Rubin architecture, which is projected to offer substantial computational power. However, Nvidia's general-purpose nature suggests it will likely cede the top position in pure performance-per-watt efficiency to hyper-specialized ASICs tailored for specific AI workloads.
A significant 'leapfrog' event in AI acceleration is highly probable by the end of 2026. This potential disruption is driven by vertically integrated or specialized competitors. Google is considered the most probable candidate to maintain its >2x performance-per-watt advantage for large-scale coding model inference. Concurrently, Moore Threads, a state-backed challenger, aims for a tenfold improvement in energy efficiency for its 2026 'Lushan' architecture. While ambitious, such a breakthrough, if coupled with a robust software stack, could significantly disrupt the market and represents a high-risk, high-reward scenario in the rapidly evolving AI hardware competition.
9. What Are the Next-Gen AI Coding Model Targets for EOY 2026?
| OpenAI EOY 2026 LiveBench Target | 90-95% |
|---|---|
| Google EOY 2026 LiveBench Target | 85%+ |
| Anthropic EOY 2026 LiveBench Target | 85-90% |
The competition for the leading AI coding model by the end of 2026 is intensifying, with major labs setting ambitious internal performance milestones. OpenAI's GPT-5.2 Codex currently leads the LiveBench 'Coding Average' with 83.62%, establishing its strong incumbent position in the field. All leading developers, including Google and Anthropic, are focused on achieving substantial capability enhancements to secure market leadership in this rapidly evolving sector.
OpenAI, Google, and Anthropic set ambitious performance goals for successors. OpenAI aims for its successor model, either GPT-5.3 or GPT-6, to achieve 90-95% on the LiveBench 'Coding Average', specifically targeting advanced agentic and multi-step coding capabilities. Google's Gemini program is targeting over 85% for its next significant release, likely Gemini 3.0, planning to leverage architectural innovations and extensive compute resources to close existing capability gaps. Anthropic, whose Claude 4.5 Sonnet already surpasses Google's current top model, is setting a performance target of 85-90% for Claude 5.0. Their strategy emphasizes scaling with safety and enhancing core reasoning for robust enterprise reliability.
Achieving these milestones demands significant architectural and computational advancements. Reaching these high performance targets will necessitate fundamental progress in model architecture, training data, and compute strategies. This could potentially involve developing models with 5-10 trillion parameters and requiring significant test-time compute. The ultimate success in this competitive landscape may depend on which approach yields greater breakthroughs, focusing either on sophisticated agentic behavior or raw scaling capabilities.
10. What Could Change the Odds
Key Catalysts
Catalyst analysis not available.
Key Dates & Catalysts
- Expiration: December 31, 2026
- Closes: December 31, 2026
11. Decision-Flipping Events
- Trigger: Catalyst analysis not available.
13. Historical Resolutions
Historical Resolutions: 14 markets in this series
Outcomes: 2 resolved YES, 12 resolved NO
Recent resolutions:
- KXCODINGMODEL-26JAN-XAI: NO (Jan 01, 2026)
- KXCODINGMODEL-26JAN-OPEN: YES (Jan 01, 2026)
- KXCODINGMODEL-26JAN-GOOG: NO (Jan 01, 2026)
- KXCODINGMODEL-26JAN-DEEP: NO (Jan 01, 2026)
- KXCODINGMODEL-26JAN-ANTH: NO (Jan 01, 2026)
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