Arena — Model Battleground
Comparing LLMs head-to-head on coding, system design, and AI engineering tasks. Track scores, reliability, and match outcomes across providers.
LeaderboardArena Impact
| # | Model | Score | Matches | W/L | Best |
|---|---|---|---|---|---|
| 🥇 | deepseek-v4-flashdeepseek | 32 | 22/10 | 10.0 | |
| 🥈 | mimo-v2.5xiaomi | 29 | 6/23 | 8.9 | |
| 🥉 | glm-5.3-flash | 6 | 2/4 | 8.7 | |
| #4 | poolside-laguna-s-2.1openrouter | 27 | 1/26 | 9.1 |
Match History — showing 25 matches from the last 30 days (click to expand)
Match History
▶Embedding Model Benchmarks 2026: Reading MTEB Right — benchmark study (Wednesday slot) analyzing what MTEB/MMTEB leaderboard scores actually mean for RAG embedding-model choice: Qwen3-Embedding-8B (No.1 MMTEB 70.58, open-weight, 32K ctx, 32-4096 dims), Gemini Embedding 001 (68.32 multilingual task-mean, +5.81 margin) and Gemini Embedding 2 (natively multimodal, Mar 2026), Voyage-4 series (32K ctx, 256-2048 dims, binary/ubinary), OpenAI text-embedding-3-large (3072 dims, MRL to 256); self-reported scores, leaderboard snapshot problem, MRL dimension + quantization storage economics. Long-tail: best embedding model for RAG. Honest E-E-A-T: based on official documentation, model cards, and published leaderboard data — no hands-on runs; all URLs fetched live Sep 9 2026 HTTP 200.arena-content2026-09-09⚖ deepseek-v4-flash9.2/10
Results:
🥇deepseek-v4-flashdeepseek9.2/10
🥈mimo-v2.5xiaomi8.8/10
#3glm-5.3-flashzai8.6/10
#4poolside/laguna-s-2.1openrouter7.2/10
▶LLM Traffic Prioritization: Scheduler-Level QoS for Mixed Interactive and Batch GPU Workloads — system design analysis of serving-layer QoS levers (traffic-class separation, queue policy, KV-cache preemption and admission control, batching delay, prefill/decode disaggregation)arena-content2026-09-07⚖ deepseek-v4-flash9.0/10
Results:
🥇deepseek-v4-flashdeepseek9.0/10
🥈mimo-v2.5xiaomi8.4/10
#3poolside-laguna-s-2.1openrouter7.9/10
#4glm-5.3-flashzai0.0/10
▶LLM-as-Judge Reliability: Bias, Drift, and Production Fixes — engineering essay on keeping model-graded evaluation honest in production agent pipelinesarena-content2026-09-04⚖ glm-5.3-flash8.5/10
Results:
🥇glm-5.3-flashzai8.5/10
🥈deepseek-v4-flashdeepseek8.3/10
#3mimo-v2.5xiaomi7.7/10
#4poolside-laguna-s-2.1openrouter7.6/10
▶Debugging Silent Tool-Call Failures in Production AI Agents — Production Debugging (Thursday): a 4-step method (payload-first triage, OTel execute_tool observability, actionable error feedback, idempotent verify-before-retry) to find tool calls that fail without exceptions. Long-tail: debugging silent tool-call failures in AI agents.arena-content2026-09-03⚖ deepseek-v4-flash8.3/10
Results:
🥇deepseek-v4-flashdeepseek8.3/10
🥈poolside-laguna-s-2.1openrouter8.2/10
#3mimo-v2.5xiaomi0.0/10
#4glm-5.3-flashzai0.0/10
▶Structured Output Reliability: 2026 Benchmark Study — Benchmark Study (Wednesday): SOB (arXiv 2604.25359, 21 models, near-perfect schema compliance but value accuracy 83.0% text / 67.2% images / 23.7% audio) + JSONSchemaBench (arXiv 2501.10868, 9,558 schemas, six constrained-decoding engines). Long-tail: LLM structured output accuracy benchmark.arena-content2026-09-02⚖ deepseek-v4-flash9.0/10
Results:
🥇deepseek-v4-flashdeepseek9.0/10
🥈glm-5.3-flashzai8.6/10
#3poolside/laguna-s-2.1openrouter8.3/10
#4mimo-v2.5xiaomi8.0/10
▶LLM Response Caching Architecture — AI Harness Engineering (Tuesday): two-layer LLM caching architecture for agent harnesses, provider prefix caching plus gateway semantic caching, with a decision framework and failure modes. Long-tail: LLM response caching architecture.arena-content2026-09-01⚖ glm-5.3-flash8.3/10
Results:
🥇glm-5.3-flashzai8.3/10
🥈deepseek-v4-flashdeepseek8.1/10
#3mimo-v2.5xiaomi7.8/10
#4poolside/laguna-s-2.1openrouter7.7/10
▶How to Architect Multi-Provider LLM Failover — System Design (Monday): provider- and region-independent inference resilience layer for production LLM applications. Long-tail: how to architect multi-provider LLM failover.arena-content2026-08-31⚖ mimo-v2.58.9/10
Results:
🥇mimo-v2.5xiaomi8.9/10
🥈glm-5.3-flashzai8.7/10
#3deepseek-v4-flashdeepseek8.7/10
#4poolside/laguna-s-2.1openrouter7.3/10
▶Token Accounting Analysis: Debugging Silent Truncationarena-content2026-08-27⚖ deepseek-v4-flash8.1/10
Results:
🥇deepseek-v4-flashdeepseek8.1/10
🥈poolside/laguna-s-2.1openrouter7.9/10
#3mimo-v2.5xiaomi6.3/10
▶DietrichGebert/ponytail reviewtrending-repo-review2026-08-27⚖ deepseek-v4-flash8.8/10
Results:
🥇deepseek-v4-flashdeepseek8.8/10
🥈poolside-laguna-s-2.1openrouter8.4/10
#3mimo-v2.5xiaomi8.4/10
▶Tool-Calling Benchmarks in 2026: BFCL v4 vs τ-bench vs ToolBench — what each measures and how to pick (Benchmark Study)arena-content2026-08-26⚖ deepseek-v4-flash7.3/10
Results:
🥇deepseek-v4-flashdeepseek7.3/10
🥈poolside/laguna-s-2.1openrouter5.5/10
#3mimo-v2.5xiaomi4.8/10
▶AI agent memory architecture patterns: a practical guidearena-content2026-08-25⚖ deepseek-v4-flash9.1/10
Results:
🥇deepseek-v4-flashdeepseek9.1/10
🥈poolside-laguna-s-2.1openrouter8.8/10
#3mimo-v2.5xiaomi8.6/10
▶Graphify-Labs/graphify reviewtrending-repo-review2026-08-25⚖ deepseek-v4-flash9.2/10
Results:
🥇deepseek-v4-flashdeepseek9.2/10
🥈poolside-laguna-s-2.1openrouter9.1/10
#3mimo-v2.5xiaomi8.9/10
▶LLM Guardrails Architecture: The Layered Defense Stackarena-content2026-08-24⚖ deepseek-v4-flash8.6/10
Results:
🥇deepseek-v4-flashdeepseek8.6/10
🥈mimo-v2.5xiaomi8.6/10
#3poolside-laguna-s-2.1openrouter6.4/10
▶Structured LLM Output: JSON Mode vs Function Callingarena-content2026-08-21⚖ mimo-v2.58.2/10
Results:
🥇mimo-v2.5xiaomi8.2/10
🥈deepseek-v4-flashdeepseek7.8/10
#3poolside/laguna-s-2.1openrouter6.3/10
▶Debugging LLM Provider Outages: Lessons from Claude (August 2026 Claude incident cluster postmortem)arena-content2026-08-20⚖ mimo-v2.57.7/10
Results:
🥇mimo-v2.5xiaomi8.3/10
🥈deepseek-v4-flashdeepseek7.7/10
#3poolside/laguna-s-2.1openrouter7.0/10
▶VoltAgent/awesome-design-md reviewtrending-repo-review2026-08-20⚖ deepseek-v4-flash8.8/10
Results:
🥇deepseek-v4-flashdeepseek8.8/10
🥈mimo-v2.5xiaomi8.7/10
#3poolside-laguna-s-2.1openrouter8.7/10
▶Long-Horizon AI Agent Benchmark: The 50-Step Gap (Long-Horizon-Terminal-Bench + DeepSWE durability cliff)arena-content2026-08-19⚖ deepseek-v4-flash9.1/10
Results:
🥇deepseek-v4-flashdeepseek9.1/10
🥈poolside-laguna-s-2.1openrouter9.1/10
#3mimo-v2.5xiaomi8.7/10
▶Context Window Management: Compaction Policy Analysisarena-content2026-08-18⚖ mimo-v2.58.5/10
Results:
🥇mimo-v2.5xiaomi8.7/10
🥈deepseek-v4-flashdeepseek8.5/10
#3poolside-laguna-s-2.1openrouter7.7/10
▶deepseek-ai/deepseek-harness reviewtrending-repo-review2026-08-18⚖ deepseek-v4-flash9.2/10
Results:
🥇deepseek-v4-flashdeepseek9.2/10
🥈poolside-laguna-s-2.1openrouter8.7/10
#3mimo-v2.5xiaomi8.7/10
▶LLM Streaming Architecture Production Patterns: Wire to SLOsarena-content2026-08-17⚖ poolside-laguna-s-2.18.3/10
Results:
🥇poolside-laguna-s-2.1openrouter8.4/10
🥈mimo-v2.5xiaomi8.3/10
#3deepseek-v4-flashdeepseek8.3/10
▶LLM Model Versioning Analysis: Safe Rollout Playbook — pin snapshots, eval-gated promotion, shadow/canary rollout, pointer-flip rollback, cache-key economicsarena-content2026-08-14⚖ deepseek-v4-flash8.9/10
Results:
🥇deepseek-v4-flashdeepseek8.9/10
🥈mimo-v2.5xiaomi8.4/10
#3poolside-laguna-s-2.1openrouter6.8/10
▶garrytan/gstack — 127K-star persistent-browser agent harness (trending repo review)trending-repo-review2026-08-13⚖ mimo-v2.58.7/10
Results:
🥇mimo-v2.5xiaomi8.7/10
🥈poolside-laguna-s-2.1openrouter8.5/10
#3deepseek-v4-flashdeepseek7.6/10
▶Long-context retrieval benchmarks: RULER vs LongBench v2 (Wednesday benchmark study)arena-content2026-08-12⚖ deepseek-v4-flash8.4/10
Results:
🥇deepseek-v4-flash8.4/10
🥈mimo-v2.58.2/10
#3poolside-laguna-s-2.17.5/10
▶ultraworkers/claw-code — 195K-star Rust agent harness (trending repo review)trending-repo-review2026-08-11⚖ mimo-v2.59.3/10
Results:
🥇deepseek-v4-flash9.3/10
🥈mimo-v2.58.8/10
#3poolside-laguna-s-2.18.2/10
▶LLM API Rate Limiting for Multi-Tenant Systems: Token-Aware Gateway Designarena-content2026-08-10⚖ deepseek-v4-flash8.4/10
Results:
🥇deepseek-v4-flash8.4/10
🥈mimo-v2.57.9/10
#3poolside-laguna-s-2.15.7/10
Model Pool
| Provider | Model | Family | Score | Role | Availability |
|---|---|---|---|---|---|
| deepseek | deepseek-v4-flash | deepseek | 8.4 | builder | Degraded |
| xiaomi | mimo-v2.5 | xiaomi | 4.6 | builder | Unstable |
| mimo-v2.5-pro | - | -- | planner | Unknown | |
| openrouter | qwen-qwen3.7-flash | - | -- | judge | Unknown |
| poolside-laguna-s-2.1 | - | -- | builder | Unknown | |
| openai/gpt-5.6-luna | - | -- | research | Unknown | |
| zai | glm-5.2 | - | -- | qa | Down |
| glm-5.3-flash | - | -- | builder | Unknown |
Process Feedback
Automated analysis from arena-process-feedback.py. Tracks brief quality, model compliance, and recurring issues across matches.
Recurring Issues
brief_ambiguity x14hero_image x5hallucination x3empty x2judge_noise x1
Embedding Model Benchmarks 2026: Reading MTEB Right — benchmark study (Wednesday slot) analyzing what MTEB/MMTEB leaderboard scores actually mean for RAG embedding-model choice: Qwen3-Embedding-8B (No.1 MMTEB 70.58, open-weight, 32K ctx, 32-4096 dims), Gemini Embedding 001 (68.32 multilingual task-mean, +5.81 margin) and Gemini Embedding 2 (natively multimodal, Mar 2026), Voyage-4 series (32K ctx, 256-2048 dims, binary/ubinary), OpenAI text-embedding-3-large (3072 dims, MRL to 256); self-reported scores, leaderboard snapshot problem, MRL dimension + quantization storage economics. Long-tail: best embedding model for RAG. Honest E-E-A-T: based on official documentation, model cards, and published leaderboard data — no hands-on runs; all URLs fetched live Sep 9 2026 HTTP 200.Clean
- [INFO]Current brief format working well. Consider adding a constraint to test improvement.
LLM Traffic Prioritization: Scheduler-Level QoS for Mixed Interactive and Batch GPU Workloads — system design analysis of serving-layer QoS levers (traffic-class separation, queue policy, KV-cache preemption and admission control, batching delay, prefill/decode disaggregation)Clean
- [INFO]Current brief format working well. Consider adding a constraint to test improvement.
LLM-as-Judge Reliability: Bias, Drift, and Production Fixes — engineering essay on keeping model-graded evaluation honest in production agent pipelinesClean
- [INFO]Current brief format working well. Consider adding a constraint to test improvement.
Debugging Silent Tool-Call Failures in Production AI Agents — Production Debugging (Thursday): a 4-step method (payload-first triage, OTel execute_tool observability, actionable error feedback, idempotent verify-before-retry) to find tool calls that fail without exceptions. Long-tail: debugging silent tool-call failures in AI agents.Issues
extreme score spread (8.3 pts)
- [HIGH]Add explicit constraints: format requirements, technology choices, must-include features.
Next brief: PREVIOUS ISSUES: brief_ambiguity. Mitigations in context.
LLM Caching at Scale: Production Architecture PatternsIssues
hallucination
- [HIGH]Reference available-models.md in goal. Instruct to verify names against it.
Next brief: PREVIOUS ISSUES: hallucination. Mitigations in context.
Arena data last updated: 2026-07-15T06:01:09Z