LLM Comparison 2026 Claude Opus 4.6, GPT-5.4, Gemini 2.5 Pro, DeepSeek V4, Llama 4 Maverick, Mistral Large 3, and Grok 4 — benchmarks, pricing, and real-world performance
LLM API costs dropped ~80% between early 2025 and April 2026 as open-weight and cost-optimised models matured. Frontier models now cluster above 88% MMLU, making SWE-bench Verified the primary differentiator for engineering teams. This comparison draws from SWE-bench, Artificial Analysis, LMSYS Chatbot Arena, and official provider documentation.
Frontier LLMs
The leading large language models as of April 2026, evaluated across key dimensions for production use.
| Dimension | Claude Opus 4.6 | GPT-5.4 | Gemini 2.5 Pro | DeepSeek V4 | Llama 4 Maverick | Mistral Large 3 | Grok 4 |
|---|---|---|---|---|---|---|---|
| Context window | 1M tokens | 128K tokens | 2M tokens | 1M tokens | 10M tokens | 262K tokens | 256K tokens |
| Input price (per 1M tokens) | $5.00 | $2.50 | $1.50 | $0.30 | $0.05–$0.90 (third-party) | $0.50 | $3.00 |
| Output price (per 1M tokens) | $25.00 | $15.00 | $6.00 | $0.50 | Free (self-host) or third-party rates | $1.50 | $15.00 |
| SWE-bench Verified | 80.8% (Opus 4.6) | 76.9% (GPT-5.4) | 80.6% (Gemini 2.5 Pro, April 2026) | 81% (DeepSeek V4) | ~65% (Maverick, self-reported) | ~50% (Codestral 2508) | ~72% (Grok 4, xAI estimate) |
| Reasoning (MMLU / GPQA) | 90.5% MMLU — strong multi-step chain-of-thought | 91.4% MMLU, 92.0% GPQA — top-tier across benchmarks | 94.1% MMLU, 94.3% GPQA Diamond (Gemini 3.1 Pro) | ~89% MMLU — near-frontier at fraction of cost | ~85% MMLU — competitive open-weight reasoning | ~82% MMLU — solid for open-source tier | ~88% MMLU — strong real-time data advantage |
| Multimodal support | Text + images | Text + images + audio + video (GPT-5.4) | Text + images + audio + video (native 2M context) | Text only (V4); DeepSeek-VL2 for vision | Text + images + video (Maverick is natively multimodal) | Text only (Pixtral for vision, separate model) | Text + images |
| Open weights | No | No | No (Gemma series is open) | Yes — Apache 2.0 (V3 series); V4 pending | Yes — Meta Llama license (Maverick & Scout) | Yes — Apache 2.0 | No |
| API availability | Anthropic API, AWS Bedrock, Google Vertex | OpenAI API, Azure OpenAI | Google AI API, Vertex AI | DeepSeek API, many third-party providers | Together AI, Groq, Fireworks, Hugging Face, self-host | Mistral API, Azure, self-host | xAI API only |
| Best for | Long-context reasoning, code generation, autonomous coding agents | General-purpose tasks, broad ecosystem, audio/voice apps | Multimodal tasks, Google Workspace, 2M-context analysis | Cost-sensitive coding and reasoning at scale | Privacy-first self-hosting, ultra-long context (10M tokens) | European data residency, open-source stacks, coding (Codestral) | Real-time data via X platform, 256K reasoning tasks |
When to choose each
Claude Opus 4.6
- Full-repo autonomous coding via Claude Code (80.8% SWE-bench)
- 1M-context analysis of large codebases or legal documents
- Enterprise reasoning with strong instruction following
- Agentic multi-step workflows via the Anthropic API
GPT-5.4
- General-purpose assistant tasks across any domain
- Teams already in the OpenAI / Azure ecosystem
- Audio input and voice-enabled applications
- Broadest third-party plugin and integration support
Gemini 2.5 Pro
- Analysing images, audio, and video in a single 2M-context prompt
- Google Workspace automation and Docs / Sheets integration
- Projects needing the largest context window from a closed model
- Cost-optimised inference via Gemini Flash variants
DeepSeek V4
- High-volume coding or reasoning at 1/10th the cost of GPT-5.4
- 81% SWE-bench score — highest among budget models
- Self-hosted deployments via open-weight V3 series
- Teams wanting near-frontier performance on a tight budget
Llama 4 Maverick
- Privacy-sensitive workloads requiring on-premise deployment
- Ultra-long context tasks — 10M tokens (Maverick)
- Air-gapped or regulated environments
- Lowest cost per token at scale via self-hosting
Mistral Large 3
- European data residency and GDPR-first deployments
- Multilingual applications across EU languages
- Code-heavy tasks via Codestral 2508 (256K context)
- Open-source stacks requiring Apache 2.0 licensed models
Grok 4
- Real-time social media monitoring and X platform integration
- Applications needing live web data without retrieval plugins
- Teams on the xAI platform with $25 free credits
Our verdict
No single model wins across all dimensions in April 2026. For autonomous coding, DeepSeek V4 (81% SWE-bench) and Claude Opus 4.6 (80.8%) lead on benchmarks — with DeepSeek winning on price. For multimodal and long-context tasks, Gemini 2.5 Pro's 2M context and native video support are unmatched. For privacy-first or constrained budgets, Llama 4 Maverick (open weights, 10M context) is the standout. Mistral remains the top European-compliance open-source pick.
Sources & References
- 01SWE-bench Leaderboard
Canonical benchmark for evaluating LLMs on real-world software engineering tasks
- 02Artificial Analysis — LLM Benchmarks & Pricing
Independent quality, speed, and price comparisons across providers
- 03LMSYS Chatbot Arena
Human preference rankings via blind A/B comparisons
- 04Anthropic Pricing
Official Claude model pricing (Claude Opus 4.6: $5/$25 per 1M)
- 05OpenAI API Pricing
Official GPT model pricing (GPT-5.4: $2.50/$15 per 1M)
- 06Google Gemini API Docs
Gemini 2.5 Pro: 2M context, $1.50/$6.00 per 1M tokens
- 07DeepSeek API Pricing
DeepSeek V4: $0.30/$0.50 per 1M tokens, 1M context
- 08Meta Llama 4 — Maverick & Scout
Llama 4 Maverick: 10M context, open weights, natively multimodal
- 09Mistral AI Pricing
Mistral Large 3: $0.50/$1.50; Codestral 2508: $0.60/$1.80 per 1M
- 10xAI API — Grok
Grok 4: $3.00/$15.00 per 1M tokens, 256K context
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