LLM Performance Comparison 2026: GPT-5, Claude, Gemini, Grok & DeepSeek Benchmarked

When people ask “which LLM performs best,” they’re usually bundling four different questions into one: How good is its reasoning and coding? How fast does it respond? How much does it cost to run at scale? And how reliably does it hold up on real, messy tasks instead of clean benchmark problems.

No single model wins on all four in 2026. The frontier closed models (GPT-5.6, Claude Opus 4.8, Gemini 3.1 Pro) trade the lead in reasoning and coding benchmarks month to month. Open-weight models like DeepSeek V4 and Qwen3.7 Max have closed most of the quality gap while costing a fraction as much per token. And raw speed — tokens generated per second — is a completely separate axis where smaller, distilled models like Llama 4 Scout dominate everything else on this list.

This article breaks performance into the four dimensions that actually change which model you should pick: coding, math/reasoning, speed, and cost-efficiency. For a comparison of what each model *knows* — context window size, training data freshness, and hallucination behavior — see our companion piece, LLM Knowledge Comparison 2026.

The table below summarizes where each major model currently stands. Figures are drawn from third-party leaderboards — LLM-Stats.com and Vellum AI’s LLM Leaderboard — as of July 2026, not vendor marketing claims. Benchmark scores move as providers ship new checkpoints, so treat exact percentages as directional rather than permanent.

GPQA Diamond measures graduate-level science reasoning; SWE-bench Verified measures whether a model’s code actually resolves real GitHub issues; AIME measures competition-level math. A model can lead one and trail another — that’s the whole point of comparing more than one number.

SWE-bench Verified is the benchmark that matters most for coding: it gives a model a real GitHub issue and checks whether its patch actually resolves it, not whether the code merely looks plausible. As of mid-2026, Anthropic’s Claude models (Fable 5 and Opus 4.8) consistently lead or sit near the top of SWE-bench Verified, reported in the 88–95% range depending on the exact checkpoint and evaluation harness.

What’s changed more than the leader is the gap beneath it. DeepSeek V4 Pro, an MIT-licensed open-weight model, reports SWE-bench Verified scores around 80% — within striking distance of closed frontier models — while costing roughly 30–35x less per million tokens. For teams running high-volume coding agents (PR review bots, automated refactors, CI triage), that cost delta often matters more than the last few benchmark points.

If your workflow is “generate one correct patch, once, for a hard problem,” pick from the frontier tier (Claude Opus 4.8, GPT-5.6). If your workflow is “run thousands of coding tasks per day and eyeball the diffs,” DeepSeek V4 Pro or Qwen3.7 Max often deliver better performance per dollar.

On pure math reasoning (AIME-style competition problems), OpenAI’s GPT-5 family has reported near-perfect or perfect scores on recent AIME sets, and GPT-5.2 Pro specifically was reported to hit 100% on AIME 2025 problems. On GPQA Diamond (graduate-level science reasoning across physics, chemistry, and biology), scores across the frontier tier are tightly clustered — Claude, GPT-5.6, and Gemini 3.1 Pro all report scores in the low-to-mid 90s (percent), with the exact ranking shifting between evaluation runs.

The practical takeaway: for pure math and formal reasoning tasks, differences between top-tier models are now small enough that prompt quality, tool access (calculators, code execution), and problem framing usually matter more than which frontier model you picked.

Speed is where the model list reshuffles completely. The frontier reasoning models — the ones winning GPQA and SWE-bench — are not the fastest models to actually run. Meta’s Llama 4 Scout is reported to generate over 2,600 tokens per second on optimized inference hardware, with latency under half a second to first token — an order of magnitude faster than most frontier closed models.

DeepSeek V4 Flash (the smaller, faster sibling of DeepSeek V4 Pro) is reported around 108–175 tokens per second depending on load, while staying inexpensive. For latency-sensitive products — live chat, voice agents, autocomplete-style tools — raw speed can matter more than an extra few points on a reasoning benchmark nobody will notice in production.

If you’re building something where the user is staring at a spinner, benchmark your own latency under realistic load before committing to a “smartest” model that turns out to feel sluggish.

This is the dimension that changed the most in 2026. A year ago, “good performance” meant “expensive.” That’s no longer reliably true. DeepSeek V4 Pro delivers coding performance within roughly 10–15 points of closed frontier models while costing on the order of 30x less per million tokens — a gap large enough to change the economics of running agents at scale, not just the sticker price of a single API call.

Google’s Gemini 3.1 Pro is frequently cited as the best cost-to-performance ratio *within* the closed frontier tier — strong reasoning scores at roughly half the input price of GPT-5.6 or Claude Opus 4.8. If you need frontier-level quality but are cost-sensitive, that combination is usually the first one worth testing.

For genuinely low-cost, high-volume workloads (classification, extraction, simple agent steps), smaller open-weight models running on your own infrastructure can push cost per task down close to zero beyond hardware — at the price of doing your own hosting and reliability engineering.

There is no universal winner — match the model to the constraint that actually bites for your workflow:

Highest-stakes single answer (legal review, security audit, hard bug): use a frontier closed model — Claude Opus 4.8 or GPT-5.6 — and budget for the higher per-token cost.

High-volume coding agent or PR bot: DeepSeek V4 Pro or Qwen3.7 Max, benchmarked against your own test suite before committing.

Latency-critical, user-facing product: Llama 4 Scout or a distilled/flash-tier model, self-hosted or served through a low-latency provider.

General agentic workflows with tool use (the kind covered across NextFlows recipes): Claude Sonnet 5 or Gemini 3.1 Pro tend to offer the best balance of reasoning, speed, and price for everyday automation.

Whatever you pick, re-run your own evaluation set periodically — every model on this list has shipped at least one new checkpoint in the last six months, and rankings move.