LLM Benchmark Scores 2026: Coding, Math & Reasoning

Last checked: June 8, 2026. This page is now maintained as a living 2026 benchmark snapshot, not a February 2026 one-off comparison.

If you searched for LLM benchmark scores 2026 coding math reasoning comparison, the short version is this: the best model depends less on one leaderboard and more on the task you are trying to automate.

Coding bug fixes, algorithm contests, math reasoning, and terminal-agent workflows are no longer measured by the same scoreboard. GPT-5.5, Claude Opus 4.8, Gemini 3.1 Pro, and DeepSeek V3.2 all look strong in different places. The useful move is to compare the right benchmark for the job.

LLM Coding Benchmarks 2026: Quick Map

The phrase LLM coding benchmarks covers several different tests. SWE-bench measures real repo bug fixing, Aider measures multi-language edit quality, LiveCodeBench measures programming challenge performance, and Terminal-Bench measures command-line agent execution.

That is why a useful LLM coding benchmark 2026 comparison should not collapse everything into one score. Match the benchmark to your workflow first, then compare GPT, Claude, Gemini, DeepSeek, and open-model results inside that context.

Current LLM Benchmark Scores 2026

This snapshot separates public leaderboards from vendor-reported release metrics. That distinction matters: a raw model, a coding agent, and an IDE assistant can post very different scores even when they use the same underlying model.

The headline: GPT-5.5 is the strongest launch-data story for terminal and scientific reasoning; Claude Opus 4.8 is the newest Anthropic agentic coding model; Gemini 3.1 Pro is the latest Google multimodal reasoning model; DeepSeek V3.2 is the value model to watch. For a broader archive path, see the AI Model Comparisons topic hub.

Coding Benchmark Scores

Coding benchmarks split into three useful families:

SWE-bench: Real Repository Repair

SWE-bench remains the most important public benchmark for real bug-fixing ability because it evaluates whether a system can resolve actual repository issues. The public leaderboard reports % Resolved, not a generic "coding IQ" score.

That distinction matters. A high SWE-bench entry often reflects both the model and the scaffold around it: retrieval, file editing, tool calls, test execution, retry policy, and patch validation. This is why Claude-family models, Gemini 3 entries, GPT-5.2 Codex entries, Kimi K2.5, and DeepSeek variants can all appear in the same public ecosystem without producing a simple model-only ranking.

If your workflow is close to "fix this real bug in this repo," SWE-bench is the right starting point. If your workflow is "edit this file cleanly in my IDE," Aider is often more useful.

Aider Polyglot: Editing Quality

The Aider leaderboard is especially useful because it tests whether a model can produce usable edits across programming languages. As of this refresh, the notable rows are:

This is why I would not read "best coding model" as one universal claim. If you are doing patch-heavy work, GPT-5-style Aider results matter. If you are doing autonomous repo repair, SWE-bench and your agent scaffold matter more.

For hands-on OpenAI workflow tactics, the older but still useful companion piece is GPT-5 for Coding: Benchmarks, Pricing & 5 Pro Tips.

LiveCodeBench: Algorithms and Contamination Resistance

LiveCodeBench is valuable because it continuously adds programming problems, which reduces training-set contamination risk compared with static benchmarks.

In the current public generation dataset, recent strong entries include o4-mini high, Gemini 2.5 Pro 06-05, o3 high, DeepSeek-R1-0528, and Qwen3 variants. That does not automatically mean those are the best production coding assistants. It means they are strong at competitive-programming-style generation.

Use LiveCodeBench when your question is "which model solves algorithmic coding problems?" Use SWE-bench or Aider when your question is "which model can change my codebase correctly?"

Math and Scientific Reasoning Scores

Math and scientific reasoning are now a separate comparison from general coding.

GPT-5.5's release data reports 51.7% on FrontierMath Tier 1-3 and 35.4% on FrontierMath Tier 4, alongside 58.6% on SWE-Bench Pro. Those numbers point to a model optimized for hard, multi-step scientific and agentic work rather than only short-form coding prompts.

DeepSeek V3.2's model card reports 74.24 on GPQA Diamond for V3.2 and 82.4 for V3.2-Speciale, plus 85 on MMLU-Pro. That makes DeepSeek much harder to dismiss as a budget-only option. It is a serious reasoning model, with the usual caveat that deployment, latency, tool use, and data policy still matter.

Gemini 3.1 Pro should be read as a native multimodal reasoning model from Google's latest Gemini 3 generation. If your math workflow includes diagrams, visual context, long documents, or multimodal inputs, raw text-only leaderboard comparisons will miss part of the picture. The practical companion article here is Gemini Deep Thinking API: Build Math AI Apps.

Agentic and Terminal Workflow Scores

Agentic coding is not the same as autocomplete.

Terminal-Bench, SWE-Bench Pro, and real coding-agent evaluations ask whether a model can keep track of a task, use tools, inspect outputs, recover from errors, and finish useful work. GPT-5.5's 82.7% Terminal-Bench 2.0 number is a strong signal for this category.

Claude Opus 4.8 is also highly relevant here because Anthropic positions it around coding, agents, professional work, and a 1M context window. That context length matters when the model must keep a large repo, a long incident, or a multi-hour task in scope.

This is also where tool choice matters. Cursor, Claude Code, Codex-style terminal agents, and custom scaffolds can change the result. The related question is not only "which model?" but "which model inside which workflow?" For the productivity trap behind this, read AI Coding Tools: 19% Slower Despite Feeling Faster.

What Changed Since February 2026

The February version of this article treated Claude Opus 4.5, GPT-5.2, Gemini 2.5 Pro, and DeepSeek V3.1 as the central comparison set. That is no longer fresh enough for a 2026 benchmark page.

Here is the June 2026 update:

The open-model story also changed. Kimi K2.5, Qwen, GLM, MiniMax, and DeepSeek entries now appear often enough in public leaderboards that they should be treated as real options, not curiosities. The Moonshot angle is covered separately in Verbose AI Beats Fast AI: Moonshot K2 $1,172 Paradox.

Which Model Should You Use?

For most teams, the best stack is hybrid:

1. Use a fast, cheaper model for routine edits and explanations.
2. Use a stronger reasoning model for hard bugs, architecture, and multi-step agent runs.
3. Validate every model output with tests, review, and observability.
4. Re-check benchmark data monthly, because the public leaderboard can change faster than your procurement cycle.

Benchmark Caveats

Benchmark scores are useful, but they are not buyer's guides by themselves.

Scaffolding changes the score. SWE-bench entries often include a model plus an agent system. Retrieval, file selection, tool execution, retries, and test harnesses can move the result dramatically.

Aider, SWE-bench, and LiveCodeBench measure different work. A model can be excellent at code edits and weaker at real repo bug repair. Another can solve algorithm contests but struggle with messy production code.

Vendor launch scores and public leaderboards should not be merged blindly. Keep them in separate rows unless the task, dataset, and evaluation harness are identical.

Latency and cost are invisible in many summaries. A model that gets a higher score with heavy reasoning may be the wrong choice for real-time IDE use.

Freshness matters. A 2026 benchmark article should name the data-check date. If this page is more than a month old when you read it, verify the latest leaderboard before making a serious model decision.

Bottom Line

The best 2026 LLM benchmark comparison is not "Claude vs GPT vs Gemini vs DeepSeek" as a single fight. It is a map:

• GPT-5.5 looks strongest in OpenAI's June 2026 launch data for terminal-agent and scientific reasoning tasks.
• Claude Opus 4.8 is the newest Anthropic frontier model for coding, agents, professional work, and large-context tasks.
• Gemini 3.1 Pro is Google's current multimodal reasoning model-card target.
• DeepSeek V3.2 is the value contender that now deserves serious coding and reasoning pilots.
• Aider, SWE-bench, LiveCodeBench, and Terminal-Bench answer different questions, so use the benchmark that matches your workflow.

If you only remember one rule: choose the benchmark that looks like your actual task, then choose the model.

Sources

• OpenAI: Introducing GPT-5.5
• Anthropic: Claude Opus 4.8
• Google DeepMind: Gemini 3.1 Pro Model Card
• DeepSeek V3.2 model card
• SWE-bench leaderboard
• Aider LLM leaderboard
• LiveCodeBench leaderboard