Live benchmarks (beta)
These charts use the latest published Arena runs on core PM tasks. Adjust the cost slider to favor raw quality or cost-aware value.
0 = ignore cost (pure quality). 1 = heavily weight cheaper models.
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What we actually test
Most AI benchmarks focus on synthetic exams. Our arena is grounded in the real work of Product Management and Product Leadership. Each model gets the same task, context, and formatting requirements, then we compare quality, cost, and likelihood that a senior PM would actually ship the result.
Strategy & execution
- •Vision narratives and PRDs for new initiatives
- •Roadmap briefs and trade-off writeups
- •Experiment and metrics design for growth questions
Communication & storytelling
- •Executive updates and stakeholder memos
- •Feature release briefs and change logs
- •UX copy and in-product messaging variants
How to read our benchmarks
Each model gets a score along two axes:
- Quality — can a busy senior PM use this output as-is?
- Value — quality adjusted for cost and latency.
We combine automated judging with a rubric tuned to PLA's curriculum.
Real PM Task Outputs
See actual outputs from our Arena experiments. These are real PM tasks completed by frontier AI models, anonymized and showcased for learning.
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Models in the arena
We treat models as interchangeable tools. The goal isn’t to crown a single champion, but to find the right fit for each kind of PM work.
Google Gemini
Gemini 3 Pro/Flash, Gemini 2.5 Pro/Flash, Gemini 1.5 Pro/Flash
Strong reasoning, long context, and great price/performance for strategy, experiments, and curriculum-aware coaching.
OpenAI GPT
GPT-5.2 Thinking, GPT-5.1, GPT-4o, GPT-4o mini
Strong generalists with great tool support. We lean on them for comparison baselines and structured outputs.
Anthropic Claude
Claude Opus 4.5, Claude Sonnet 4, Claude Haiku 4, Claude 3.5
Excellent for nuanced reasoning, long-form writing, and complex analysis. Known for thoughtful, well-structured outputs.
MiniMax
MiniMax M2, MiniMax M2.1
Emerging model with strong reasoning performance at competitive prices.
Kimi (Moonshot)
Kimi K2, Kimi K2.5 Thinking
Strong long-context reasoning with competitive pricing for PM tasks.
GLM (Zhipu AI)
GLM 4.6, GLM 4.7
Chinese frontier model with strong multilingual capabilities and reasoning.
NVIDIA Nemotron
Nemotron 4 Ultra, Nemotron 4 Super
New entrant from NVIDIA with strong reasoning optimized for enterprise.
xAI Grok
Grok-4 Fast (Reasoning & Non-Reasoning), Grok Code Fast 1
xAI's models with real-time data access and strong reasoning for strategic analysis.
Meta Llama
Llama 4 Maverick, Llama 4 Scout
Open-source frontier models with strong performance and flexible deployment options.
DeepSeek
DeepSeek V3, DeepSeek Chat v3.1/v3-0324
Exceptional value models with quality approaching frontier at a fraction of the cost.
Qwen, Nova & Others
Qwen3, Amazon Nova, Mistral Nemo, GLM 4.6 via OpenRouter
We track emerging models that punch above their weight on cost/value or specific PM tasks.
Small/Budget Models
GPT-4o mini, Claude Haiku, Gemini Flash Lite, Nova Lite
Ultra-light models for simple tasks at minimal cost. Great for high-volume, low-complexity work.
Why this matters for your career
As a PM, your edge isn’t in memorizing model specs. It’s in knowing how to turn AI into leverage for discovery, strategy, and execution. Our arena abstracts away the vendor noise and tells you, in plain language, where each model shines.
- Better tools, fewer experiments: stop burning time trialing models blindly. Start with a shortlist per task.
- Stronger narrative with stakeholders: explain why you chose a model in terms of reliability, quality, and cost.
- Future-proof learning: as new models ship, we’ll update the arena so you always have an up-to-date view.
We love Artificial Analysis. Our arena answers a different question.
If you want the gold-standard, ecosystem-wide view of every frontier model — intelligence indices, throughput, latency, raw pricing — go read Artificial Analysis. They're the best in the game at independent, cross-provider benchmarking, and we reference their work when we're evaluating new model releases.
Our arena is narrower by design. We don't score models on logic puzzles, code golf, or general intelligence exams — we score them on the actual work Product Leaders ship: PRDs, experiment designs, exec updates, positioning copy, and social posts. Same rubric every run. Same prompts. The rankings you see here are tuned for PM decisions, not leaderboard bragging rights.
Two lenses, one decision
- 1Artificial Analysis — best for ecosystem-wide intelligence, throughput, and cost benchmarks across every frontier model.
- 2PLA Arena — best for picking the right model for a specific PM task, graded against a curriculum-aligned PM rubric.
We use both. You probably should too.
We don’t accept affiliate fees or pay-to-play placement for model rankings. Scores are based on our own tasks, prompts, rubrics, and logs, and we’ll publish methodology updates as the ecosystem evolves.