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Benchmark Arena
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NeuralOps 7-模型 Benchmark Arena

Right 模型. Right 任务. Less 废弃物.

AINNA NeuralOps Benchmark Arena compares 7 NeuralOps models and maps each model to the right operational task. 并非每项任务都需要最大的模型. Simple tasks should use fast 轻量模型s, coding should use coding-focused models, audit and planning should use reasoning models, long documents should use long-context models, translation should use language-focused models, and classification should use fast low-cost models.

Which 模型 Should I Use? See 智能路由
自动-Looping 模型 追踪

The 7 NeuralOps models at a glance

Hover to pause. Drag with mouse or swipe on mobile. Release to resume the arena loop.

TOP-TIER AGENT

Grok 4.7

Frontier 代理 推理

88.2 Arena 分数
ADVANCED AGENT

Grok 4.6

Advanced 多-Step 代理

87.3 Arena 分数
ORCHESTRATION AGENT

Grok 4.5

代理 编排 & 推理

86.5 Arena 分数
BUILD AGENT

GLM

Coding & 分离式系统

90.2 Arena 分数
PLAN AGENT

DeepSeek R1

审计 & Strategic 分析

90 Arena 分数
BUSINESS / LANGUAGE AGENT

Mistral

翻译 & Localization

89.7 Arena 分数
FAST ROUTER

Gemma

分类 引擎

89 Arena 分数
GENERAL ASSISTANT

Qwen

Primary Conversational 界面

88.3 Arena 分数
LONG CONTEXT AGENT

Llama

Long-上下文 分析

88 Arena 分数
RESEARCH AGENT

Kimi K2

Long-上下文 研究

88 Arena 分数
TOP-TIER AGENT

Grok 4.7

Frontier 代理 推理

88.2 Arena 分数
ADVANCED AGENT

Grok 4.6

Advanced 多-Step 代理

87.3 Arena 分数
ORCHESTRATION AGENT

Grok 4.5

代理 编排 & 推理

86.5 Arena 分数
BUILD AGENT

GLM

Coding & 分离式系统

90.2 Arena 分数
PLAN AGENT

DeepSeek R1

审计 & Strategic 分析

90 Arena 分数
BUSINESS / LANGUAGE AGENT

Mistral

翻译 & Localization

89.7 Arena 分数
FAST ROUTER

Gemma

分类 引擎

89 Arena 分数
GENERAL ASSISTANT

Qwen

Primary Conversational 界面

88.3 Arena 分数
LONG CONTEXT AGENT

Llama

Long-上下文 分析

88 Arena 分数
RESEARCH AGENT

Kimi K2

Long-上下文 研究

88 Arena 分数
模型 Lineup

Every model has a clear job

The arena is not trying to crown one universal winner. It explains which model should be used, why it fits, and where it should be avoided.

TOP-TIER AGENT

Grok 4.7

88.2

Frontier 代理 推理

Use Grok 4.7 when the task is extremely hard and requires frontier-level intelligence.

优势最高点 capability frontier reasoning and agentic performance.
WeaknessHigher cost, external API latency.
最适合
  • Hardest multi-step agent tasks
  • 复杂 planning & orchestration
  • 高-stakes decisioning
  • Long-horizon tool use
  • Advanced code + reasoning hybrids
ADVANCED AGENT

Grok 4.6

87.3

Advanced 多-Step 代理

Use Grok 4.6 for sophisticated multi-step agent tasks that exceed local model capacity.

优势优秀 balance of frontier reasoning with practical agent use.
WeaknessStill external API.
最适合
  • Agentic coding & research
  • 复杂 execution flows
  • 多-tool orchestration
  • Deep problem solving
ORCHESTRATION AGENT

Grok 4.5

86.5

代理 编排 & 推理

Use Grok 4.5 as a powerful general frontier agent for complex work.

优势Strong agentic reasoning at slightly lower cost than 4.7.
Weakness外部 dependency.
最适合
  • 代理 routing & chaining
  • 高-intelligence general tasks
  • 推理 + tool calling
BUILD AGENT

GLM

90.2

Coding & 分离式系统

Use GLM when the output must become working code, 系统 logic, API wiring, or a detached automation component.

优势Strong coding and implementation focus.
WeaknessNot the best choice for business writing or translation.
最适合
  • Generic Agent AI / OpenCode build tasks
  • Coding repair
  • 后端 logic
  • 分离式 系统 development
  • API 集成
  • Debugging
PLAN AGENT

DeepSeek R1

90

审计 & Strategic 分析

Use DeepSeek R1 when the task needs careful thinking, risk review, architecture logic, or staged decision-making.

优势Strong reasoning and structured analysis.
WeaknessSlower than 轻量模型s.
最适合
  • Planning
  • 要求 audit
  • 风险 analysis
  • Deep reasoning
  • 架构 review
  • Phase breakdown
BUSINESS / LANGUAGE AGENT

Mistral

89.7

翻译 & Localization

Use Mistral when tone, readability, translation quality, and customer-facing language matter most.

优势良好 language flow and rewriting.
WeaknessNot the strongest coding repair model.
最适合
  • 翻译
  • Rewriting
  • Localization
  • Bahasa/英语 tone adjustment
  • Customer-facing copy
FAST ROUTER

Gemma

89

分类 引擎

Use Gemma as the first routing layer for simple decisions before escalating expensive work to larger models.

优势Fast and efficient.
WeaknessNot suitable for deep reasoning or complex build tasks.
最适合
  • Fast classification
  • Tagging
  • Intent detection
  • 产品 category routing
  • 首先-level filtering
  • Low-cost automation
GENERAL ASSISTANT

Qwen

88.3

Primary Conversational 界面

Use Qwen as the default conversational interface when the task is broad, mixed, or not yet classified.

优势Balanced model for daily interaction.
WeaknessNot always best for deep audit or coding-heavy work.
最适合
  • 一般 assistant
  • 聊天 interface
  • Multimodal/product review if supported
  • Customer support draft
  • 一般 business Q&A
LONG CONTEXT AGENT

Llama

88

Long-上下文 分析

Use Llama when the main challenge is keeping many sections of context coherent across a long input.

优势良好 for longer context understanding.
WeaknessCan be slower than 轻量模型s.
最适合
  • Long document reading
  • 政策/SOP review
  • Long instruction processing
  • 多-section analysis
  • 知识库 summarization
RESEARCH AGENT

Kimi K2

88

Long-上下文 研究

Use Kimi K2 when the work involves heavy research, large comparisons, or deep content review.

优势Strong long-context research handling.
WeaknessNot ideal for quick small tasks.
最适合
  • 研究-heavy tasks
  • 大 document comparison
  • Deep content review
  • Long report analysis
  • Strategic research
7 模型 Difference Matrix

理解 the difference before routing the task

模型Main 角色Best UseAvoid For速度推理Coding语言Long 上下文成本效益推荐 代理
Grok 4.7 Frontier 代理 推理 升级 the most difficult agentic, planning, and reasoning work Simple classification or high-volume cheap tasks 高 Very 高 Very 高 高 Very 高 中等 TOP-TIER AGENT
Grok 4.6 Advanced 多-Step 代理 重型 agent 工作流 that need strong step-by-step intelligence Low-value repetitive work 高 Very 高 Very 高 高 高 中等 ADVANCED AGENT
Grok 4.5 代理 编排 & 推理 核心 agent orchestration and difficult reasoning chains Fast cheap classification 高 Very 高 高 高 高 中等 ORCHESTRATION AGENT
GLM Coding & 分离式系统 构建, repair, integrate, and debug production 系统 Customer-facing copy and translation-heavy tasks 高 中等 Very 高 中等 中等 高 BUILD AGENT
DeepSeek R1 审计 & Strategic 分析 审计, strategy, planning, and high-impact reasoning Simple tagging, small rewrites, and quick low-risk replies 中等 Very 高 高 中等 高 高 PLAN AGENT
Mistral 翻译 & Localization Rewrite, translate, localize, and polish business content 后端 repair and deep architecture review 高 中等 中等 Very 高 中等 高 BUSINESS / LANGUAGE AGENT
Gemma 分类 引擎 Classify, tag, route, and filter at high speed Strategic planning, coding repair, and long research work Very 高 Low Low 中等 Low Very 高 FAST ROUTER
Qwen Primary Conversational 界面 一般 chat, business Q&A, and balanced daily assistance 高-risk audit decisions and complex code repair 高 高 中等 高 中等 高 GENERAL ASSISTANT
Llama Long-上下文 分析 Read, compare, and summarize long policy or knowledge material Short classification and fast routine routing 中等 高 中等 高 Very 高 高 LONG CONTEXT AGENT
Kimi K2 Long-上下文 研究 研究, compare, review, and synthesize large material Tiny prompts, quick tags, and first-level filtering 中等 高 中等 高 Very 高 高 RESEARCH AGENT
Usage Guide

Which model should you use?

Need the absolute hardest agent reasoning or frontier planning?

Use Grok 4.7

升级 to the top frontier model for the most complex multi-step agent work.

Need advanced multi-step agent execution or heavy research+build?

Use Grok 4.6

Powerful frontier agent for sophisticated orchestration and deep tasks.

Need strong agentic reasoning and tool use for complex problems?

Use Grok 4.5

高-intelligence agent model for demanding orchestration.

Need to build or repair a 系统?

Use GLM

Best routed to BUILD AGENT / Generic Agent AI / OpenCode implementation work.

Need audit, strategy, planning, or risk review?

Use DeepSeek R1

Best for reasoning, requirement audit, and structured phase breakdown.

Need translation, rewriting, or localization?

Use Mistral

Best for language flow, tone, and customer-facing copy.

Need fast classification or routing?

Use Gemma

Best for low-cost tagging, filtering, and intent detection.

Need general assistant or chat interface?

Use Qwen

Best balanced default for everyday business Q&A.

Need long document analysis?

Use Llama

Best when the task depends on long instruction or policy context.

Need long-context research?

Use Kimi K2

Best for research-heavy comparisons and report synthesis.

Benchmark 分类

测试 are based on real operational work

Preferred: Grok 4.7

Frontier 代理 推理 (Hardest 任务)

What is tested: Extremely complex multi-step agent 工作流, high-stakes planning, long-horizon tool use, and problems that defeat local models.

Why preferred: 最高点 frontier capability for the most difficult agentic and reasoning work.

Depth 规划 quality Tool success Correctness
Preferred: Grok 4.6

Advanced 代理 编排

What is tested: 多-agent coordination, complex execution chains, research + build hybrids.

Why preferred: Strong frontier agent performance for sophisticated orchestration.

Step accuracy 工具调用 Consistency 恢复
Preferred: Grok 4.5

Agentic Coding & Deep 研究

What is tested: 大-scale refactoring, architecture implementation, deep research synthesis with code output.

Why preferred: Powerful reasoning + coding agent for demanding build and analysis work.

实施 quality 推理 trace Completeness
Preferred: GLM

Coding and 系统 维修

What is tested: Bug fixes, API wiring, backend logic, and production patch quality.

Why preferred: Coding-focused output with stronger implementation fit.

Patch quality 构建 success Debug accuracy
Preferred: DeepSeek R1

合规 and 风险 审计

What is tested: 风险 review, requirement gaps, policy checks, and audit reasoning.

Why preferred: Best fit for structured reasoning and risk analysis.

推理 depth 风险 coverage 可追溯性
Preferred: Gemma

Fast 分类

What is tested: Intent detection, tagging, product category routing, and first-level filtering.

Why preferred: Fast, efficient, and low-cost for simple decisions.

延迟 成本 Label accuracy
Preferred: Mistral

翻译 and Rewriting

What is tested: Bahasa/英语 tone adjustment, localization, rewrite quality, and clarity.

Why preferred: Strong flow for language and customer-facing copy.

Tone Fluency Meaning retention
Preferred: Kimi K2

Long 文档 研究

What is tested: 大 report review, document comparison, and research synthesis.

Why preferred: Designed for research-heavy long-context work.

上下文 retention 来源 coverage 综合 quality
Preferred: Qwen

Primary Conversation

What is tested: 一般 chat, business Q&A, customer support drafts, and mixed daily tasks.

Why preferred: Balanced default assistant for broad interactions.

Helpfulness Tone 任务 completion
Preferred: Gemma + local + Grok escalation

智能路由 准确度

What is tested: 模型 selection based on task type, risk, context length, and output format.

Why preferred: Fast local first, escalate hard work to frontier Grok models when needed.

路线 precision 升级处理 need 成本 saved
智能路由

路线 by task, risk, context, and output format

智能路由 decides which model to use before compute is spent. Simple jobs go to fast 轻量模型s. Risky or strategic jobs go to reasoning models. 构建 tasks go to coding models. Long-context work goes to long-document models.

Hardest agent work -> Grok 4.7 Advanced agent orchestration -> Grok 4.6 复杂 agentic tasks -> Grok 4.5 分类 -> Gemma Coding repair -> GLM 审计 and strategy -> DeepSeek R1 翻译 -> Mistral 一般 conversation -> Qwen Long document analysis -> Llama Long-context research -> Kimi K2
01检测 task type
02检测 risk level
03检测 context length
04检测 output format requirement
05Select suitable model
06分数 result
07升级 to stronger model if needed
08保存 benchmark evidence
Battle 模式

Same prompt, 7 models, measurable winner

Battle 模式 can send the same prompt to multiple models. The 系统 compares quality, speed, cost efficiency, safety, human edit rate, instruction following, and format compliance. The winning model becomes evidence for a future routing rule.

Prompt->7 Models->分数->Winner->路由 规则
Scoring Formula

Arena score balances quality, efficiency, and operational risk

准确度

Correctness of the answer against the expected operational result.

任务 Fit

How naturally the model matches the workload type.

速度

响应 latency and suitability for production flow.

成本

计算 and token efficiency for the task size.

安全

风险 control, refusal discipline, and safe handling.

人类 Edit Rate

How much correction 是必需的 before use.

说明 Following

Ability to follow constraints and sequence.

格式 合规

可靠性 of JSON, tables, schema, and exact output format.

稳定性

Consistency across repeated operational runs.

升级处理 Need

How often a task must be rerouted to a stronger model.

Leaderboard

Useful winners by operational category

Best 总体 模型

GLM

最高点 blended arena score across quality, speed, cost, safety, and edit rate.

Best Frontier 代理

Grok 4.7

Top capability for the hardest agentic, planning, and reasoning tasks.

Best Coding 模型

GLM

推荐 for Generic Agent AI / OpenCode builds, repairs, APIs, and debugging.

Best Planning / 审计 模型

DeepSeek R1

Best fit for strategy, requirement audit, risk review, and architecture reasoning.

Best 翻译 模型

Mistral

Best for rewriting, localization, and Bahasa/英语 tone adjustment.

Fastest 分类 模型

Gemma

Fast, efficient first-layer classifier for low-cost automation.

Best Long-上下文 模型

Kimi K2

Strong for large document comparison and research-heavy long-context work.

Best 一般 聊天 模型

Qwen

Balanced default model for daily assistant and business Q&A.

Best 成本-高效 模型

Gemma

Best used when a simple classification should not burn premium compute.

排序模型角色推荐 代理Arena 分数
#1 GLM Coding & 分离式系统 BUILD AGENT 90.2
#2 DeepSeek R1 审计 & Strategic 分析 PLAN AGENT 90
#3 Mistral 翻译 & Localization BUSINESS / LANGUAGE AGENT 89.7
#4 Gemma 分类 引擎 FAST ROUTER 89
#5 Qwen Primary Conversational 界面 GENERAL ASSISTANT 88.3
#6 Grok 4.7 Frontier 代理 推理 TOP-TIER AGENT 88.2
#7 Llama Long-上下文 分析 LONG CONTEXT AGENT 88
#8 Kimi K2 Long-上下文 研究 RESEARCH AGENT 88
#9 Grok 4.6 Advanced 多-Step 代理 ADVANCED AGENT 87.3
#10 Grok 4.5 代理 编排 & 推理 ORCHESTRATION AGENT 86.5

The best model depends on the task.

NeuralOps reduces waste by routing the right task to the right model. The goal is not the biggest model. The goal is the model that delivers the required quality, speed, safety, and cost profile for that exact job.

Discuss NeuralOps 路由
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