# AI Model Deep Comparison — February 2026

*Compiled by Rivet, Feb 25 2026. Sources: Onyx Leaderboard, LM Arena, official pricing pages, independent reviews.*

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## Tier S — The Frontier (Best of the Best)

### 🟣 Claude Opus 4.6 (Anthropic)
| Spec | Value |
|------|-------|
| **Parameters** | Undisclosed (~200B+ est.) |
| **Context Window** | 200K tokens |
| **API Pricing** | $5/M input, $25/M output (≤200K) · $10/$37.50 (>200K) |
| **Max Output** | 32K tokens |
| **Modalities** | Text, images, code |

**Benchmark Highlights:**
- GPQA Diamond: 91.3% (graduate-level science)
- SWE-bench Verified: 80.8% (#1 in coding)
- AIME 2025: 100% (perfect math)
- ARC-AGI-2: 68.8% (abstract reasoning — best in class)
- OSWorld: 72.7% (real computer use — best by far)
- τ2-bench: 91.9% (agentic tasks)
- BrowseComp: 84.0% (web browsing capability)
- Terminal-Bench 2.0: 65.4%
- LM Arena Code: #1-2

**Strengths:** Best coding model alive. Best at agentic/autonomous tasks. Hardest to prompt inject. Best personality/nuance. Strongest at abstract reasoning. Consistent, reliable output.

**Weaknesses:** Most expensive mainstream model. Slower inference. 200K context is smallest of the frontier. No self-hosted option.

**Best For:** Critical code, agentic workflows, processing untrusted/external data, complex reasoning, Chief of Staff work.

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### 🔵 GPT-5.2 (OpenAI)
| Spec | Value |
|------|-------|
| **Parameters** | Undisclosed |
| **Context Window** | 128K-400K tokens (varies by tier) |
| **API Pricing** | $1.75/M input, $14/M output (standard) · $21/$168 (Pro) |
| **Max Output** | Variable |
| **Modalities** | Text, images, audio, code |

**Benchmark Highlights:**
- GPQA Diamond: 93.2% (#1 in science reasoning)
- SWE-bench Verified: 80.0%
- AIME 2025: 100% (perfect math)
- MMMLU: 89.6% (multilingual)
- MMMU-Pro: 80.4% (visual reasoning)
- HLE: 50.0% (Humanity's Last Exam)
- Hallucination rate: 65% lower than GPT-4o

**Strengths:** Best pure reasoner. Perfect math scores. Configurable "reasoning effort dial" (none to xhigh). Massive ecosystem of tools/integrations. Strong multimodal (text + image + audio). Cached input discounts (10x cheaper).

**Weaknesses:** 10-20 second latency on extended thinking. Pro tier pricing is brutal ($168/M output). No fine-tuning access. Slightly behind Claude on coding.

**Best For:** Pure reasoning, scientific analysis, math-heavy work, applications needing massive ecosystem support.

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### 🟢 Gemini 3.1 Pro (Google DeepMind)
| Spec | Value |
|------|-------|
| **Parameters** | 1 trillion+ |
| **Context Window** | 1M tokens (biggest production window) |
| **API Pricing** | $2/M input, $12/M output (≤200K) · $4/$18 (>200K) |
| **Max Output** | 65,536 tokens |
| **Modalities** | Text, images, audio, video, code |

**Benchmark Highlights:**
- LM Arena Overall: #1 (1490 score, 27K+ votes)
- GPQA Diamond: 91.9%
- SWE-bench Verified: 78.0% (competitive)
- AIME 2025: 100%
- ARC-AGI-2: 77.1% (3.1 Pro — massive jump)
- HLE: 45.8%
- MMMLU: 91.8% (multilingual leader)

**Strengths:** 1M token context window processes entire codebases/legal portfolios in one pass. Best multimodal (native video + audio understanding). LM Arena #1 in human preference. Excellent value at $2/$12. Native Google ecosystem integration. 65K output tokens.

**Weaknesses:** Long-context reliability not as tight as GPT-5.2's 256K window for fact retrieval. Deep Think mode adds 3-8 seconds latency. Integration ecosystem less mature than OpenAI for coding tools.

**Best For:** Document-heavy analysis, massive codebases, multimodal tasks (video/audio), long-form generation, research at scale.

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### 🔴 Grok 4.2 (xAI) — Beta
| Spec | Value |
|------|-------|
| **Parameters** | ~1 trillion (est.) |
| **Context Window** | 256K tokens |
| **API Pricing** | $3/M input, $15/M output (Grok 4) · $0.20/$0.50 (Grok 4.1 Fast) |
| **Modalities** | Text, images, code, real-time X data |

**Benchmark Highlights:**
- LM Arena: #2 overall (thinking mode, 1477 score)
- SWE-bench: 75.0%
- ARC-AGI: 15.9% (first to break 10% — older benchmark version)
- Alpha Arena Trading: #1 (12% returns, beat every AI)
- Hallucination rate: 4.22%

**Strengths:** Real-time X (Twitter) data integration — live awareness no other model has. Proven autonomous decision-making (Alpha Arena trading competition winner). Grok 4.1 Fast is insanely cheap ($0.20/$0.50). 256K context. Less content filtering = more flexible. Strong reasoning with thinking mode.

**Weaknesses:** Still in beta. Fewer safety guardrails (liability for regulated industries). EU regulatory scrutiny. Ecosystem less mature. Enterprise features lacking. Data sovereignty concerns (SpaceX/defense ties).

**Best For:** Real-time analysis, financial/market work, autonomous agents needing live data, cost-sensitive reasoning tasks (4.1 Fast).

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## Tier A — The Contenders

### 🟢 Kimi K2.5 (Moonshot AI)
| Spec | Value |
|------|-------|
| **Parameters** | 1 trillion (open-source, MIT license) |
| **Context Window** | 262K tokens |
| **API Pricing** | $0.60/M input, $3/M output |
| **Max Output** | 8,192 tokens |
| **Modalities** | Text, images, code |

**Benchmark Highlights:**
- AIME 2025: 96.1% (near-perfect math)
- SWE-bench Verified: 76.8%
- LiveCodeBench: 98.0%
- HLE-Full: 50.2% (best for agentic tasks)
- BrowseComp: 74.9%
- DeepSearchQA: 77.1%
- MMMU-Pro: 78.5% (vision)
- OCR accuracy: 92.3%

**Strengths:** Agent Swarm — spawns up to 100 sub-agents in parallel (4.5x speed). Open-source MIT license. Extremely cost-effective ($0.60/$3). Best tool-augmented reasoning (+20.1% improvement with tools). Strong vision/OCR. 262K context. Competitive on nearly every benchmark.

**Weaknesses:** China-based company (data sovereignty risk, potential export controls). 8K max output is limiting. Text-only in base mode (no native audio). Ecosystem still immature. Can be verbose on ambiguous prompts.

**Best For:** Agentic automation, parallel research tasks, vision/OCR workflows, cost-sensitive bulk processing, tool-heavy workflows.

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### 🔵 Claude Sonnet 4.6 (Anthropic)
| Spec | Value |
|------|-------|
| **Parameters** | Undisclosed |
| **Context Window** | 200K tokens |
| **API Pricing** | $3/M input, $15/M output |
| **Modalities** | Text, images, code |

**Benchmark Highlights:**
- SWE-bench Verified: 79.6%
- MMMLU: 89.3%
- MMMU-Pro: 75.6%
- HLE: 49.0%
- ARC-AGI-2: 58.3%

**Strengths:** 80% of Opus capability at 60% of the cost. Excellent coding (nearly matches Opus on SWE-bench). Fast response times. Good balance for most tasks. Same safety/reliability as Opus.

**Weaknesses:** Less nuanced than Opus on complex judgment. Slightly more susceptible to injection. 200K context limit.

**Best For:** Daily workhorse — heartbeats, routine coding, cron jobs, tasks where Opus is overkill.

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### 🔴 DeepSeek V3.2 (DeepSeek)
| Spec | Value |
|------|-------|
| **Parameters** | 685B (MoE, 37B active) |
| **Context Window** | 130K tokens |
| **API Pricing** | $0.28/M input, $0.42/M output |
| **Modalities** | Text, code |

**Benchmark Highlights:**
- GPQA Diamond: 79.9%
- SWE-bench Verified: 67.8%
- AIME 2025: 89.3%
- LiveCodeBench: 88.5%

**Strengths:** Absurdly cheap ($0.28/$0.42). Strong coding and math for the price. Open-source MIT license. MoE architecture = efficient. Trained for $6M vs OpenAI's $100M+.

**Weaknesses:** China-based (data routes through Chinese servers — multiple governments have banned it). 130K context. Text-only. Weaker creative/nuanced output. Privacy concerns are real.

**Best For:** Analytical tasks, math, coding on a budget, fallback model, non-sensitive batch processing.

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### 🔴 DeepSeek R1 (DeepSeek)
| Spec | Value |
|------|-------|
| **Parameters** | 671B (MoE) |
| **Context Window** | 128K tokens |
| **API Pricing** | $0.28/M input, $0.42/M output |
| **Modalities** | Text, code |

**Benchmark Highlights:**
- GPQA Diamond: 71.5%
- AIME 2025: 87.5% (pre-V3.2)
- LiveCodeBench: 90.8%
- Shows full reasoning chain

**Strengths:** Shows its reasoning chain (transparent thinking). Excellent for structured logic, math, step-by-step analysis. Incredibly cheap. Self-taught reasoning (no human feedback needed).

**Weaknesses:** Slow (extensive thinking). Not great for chat/creative. Same China/privacy concerns as V3.2.

**Best For:** Complex logic chains, financial analysis, mathematical reasoning, decision trees.

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### 🟡 MiniMax M2.5 (MiniMax)
| Spec | Value |
|------|-------|
| **Parameters** | 230B |
| **Context Window** | 205K tokens |
| **API Pricing** | $0.30/M input, $1.20/M output |
| **Modalities** | Text, code |

**Benchmark Highlights:**
- SWE-bench Verified: 80.2%
- AIME 2025: 86.3%
- MMMU-Pro: 85.2% (strong vision)
- LM Arena Code: #7

**Strengths:** Surprisingly strong coding (80.2% SWE-bench rivals Opus). Very cheap ($0.30/$1.20). Good vision/multimodal. 205K context.

**Weaknesses:** Less proven in production. Smaller ecosystem. Chinese company.

**Best For:** Budget coding, general-purpose work at low cost, vision tasks.

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## Tier B — Specialists & Budget Options

### Llama 4 Scout/Maverick (Meta)
| Spec | Value |
|------|-------|
| **Parameters** | 400B (Maverick MoE) |
| **Context Window** | Up to 10M tokens (Scout!) |
| **API Pricing** | $0.11/$0.34 (Scout) · $0.20/$0.60 (Maverick) |

**Strengths:** 10M token context (Scout) is insane — 7,500 pages in one pass. Open-source. Very cheap. 200 languages.

**Weaknesses:** Lower benchmark scores than tier S/A models. Best run self-hosted for full benefit.

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### Mistral Large 3 / Medium 3.1
| Spec | Value |
|------|-------|
| **Parameters** | 675B (Large) |
| **Context Window** | 256K |
| **API Pricing** | $0.50/$1.50 (Large) · $0.40/$2.00 (Medium) |

**Strengths:** 90% of premium performance at 1/8th the cost (Medium). Self-hostable. Strong European option (French company, no China/US concerns).

**Weaknesses:** Coding not competitive with top tier. Smaller community.

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### GLM-5 (Zhipu AI)
| Spec | Value |
|------|-------|
| **Parameters** | 744B |
| **Context Window** | 200K |
| **API Pricing** | $1.00/$3.20 |

**Strengths:** Strong vision (MMMU-Pro 88.0%), competitive reasoning, MIT licensed open-source variant.

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### Qwen 3.5 (Alibaba)
| Spec | Value |
|------|-------|
| **Parameters** | 397B |
| **Context Window** | 262K |

**Strengths:** Strong math, coding, multilingual. Open-source. Very competitive benchmarks.

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## 💰 Cost Comparison (Per 1M Tokens)

| Model | Input | Output | Value Rating |
|-------|-------|--------|:---:|
| DeepSeek V3.2/R1 | $0.28 | $0.42 | ⭐⭐⭐⭐⭐ |
| Grok 4.1 Fast | $0.20 | $0.50 | ⭐⭐⭐⭐⭐ |
| MiniMax M2.5 | $0.30 | $1.20 | ⭐⭐⭐⭐⭐ |
| Kimi K2.5 | $0.60 | $3.00 | ⭐⭐⭐⭐ |
| Mistral Large 3 | $0.50 | $1.50 | ⭐⭐⭐⭐ |
| GPT-5.2 | $1.75 | $14.00 | ⭐⭐⭐ |
| Gemini 3.1 Pro | $2.00 | $12.00 | ⭐⭐⭐ |
| Grok 4 | $3.00 | $15.00 | ⭐⭐⭐ |
| Sonnet 4.6 | $3.00 | $15.00 | ⭐⭐⭐ |
| Opus 4.6 | $5.00 | $25.00 | ⭐⭐ |
| GPT-5.2 Pro | $21.00 | $168.00 | ⭐ |

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## 🏆 Best Model By Task

| Task | Winner | Runner-Up | Why |
|------|--------|-----------|-----|
| **Coding** | Claude Opus 4.6 | Sonnet 4.6 / MiniMax M2.5 | 80.8% SWE-bench, #1 LM Arena Code |
| **Pure Reasoning** | GPT-5.2 | Gemini 3.1 Pro | 93.2% GPQA, perfect AIME |
| **Abstract Reasoning** | Gemini 3.1 Pro | Claude Opus 4.6 | 77.1% ARC-AGI-2 (3.1 Pro leap) |
| **Agentic/Autonomous** | Claude Opus 4.6 | Kimi K2.5 | 72.7% OSWorld, 91.9% τ2-bench |
| **Document Processing** | Gemini 3.1 Pro | Llama 4 Scout | 1M context, native multimodal |
| **Math** | GPT-5.2 = Opus 4.6 | Kimi K2.5 (96.1%) | Both hit 100% AIME |
| **Vision/OCR** | Kimi K2.5 | Gemini 3.1 Pro | 92.3% OCR, 78.5% MMMU-Pro |
| **Budget Coding** | MiniMax M2.5 | DeepSeek V3.2 | 80.2% SWE-bench at $0.30/$1.20 |
| **Budget Reasoning** | DeepSeek R1 | Grok 4.1 Fast | Shows reasoning chain, $0.28/M |
| **Real-Time Data** | Grok 4.2 | — | Only model with live X integration |
| **Safety/Injection Resistance** | Claude Opus 4.6 | GPT-5.2 | Most robust against prompt injection |
| **Multilingual** | Gemini 3.1 Pro | Opus 4.6 | 91.8% MMMLU |
| **Cost Efficiency** | DeepSeek V3.2 | Grok 4.1 Fast | $0.28/$0.42 is unbeatable |
| **Self-Hosting** | Kimi K2.5 / DeepSeek | Llama 4 / GLM-5 | Open-source MIT licensed |

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*Last updated: Feb 25, 2026*