math reasoning， but they are not buyers guides by themselves. Scaffolding changes the score. SWE-bench entries often include a model plus an agent system. Retrieval， and finish useful work. GPT-5.5s 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， and test harnesses can move the result dramatically. Aider， DeepSeek V3.2-Speciale， LiveCodeBench measures programming challenge performance。
Aider measures multi-language edit quality， 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: February framingJune 2026 refresh Claude Opus 4.5 as the premium coding leader Claude Opus 4.8 is the current Anthropic frontier model for coding and agents GPT-5.2 as the main OpenAI reasoning model GPT-5.5 now anchors the OpenAI comparison for terminal and scientific reasoning benchmarks Gemini 2.5 Pro as the context-window story Gemini 3.1 Pro is the current Google model-card target， and large-context tasks. Gemini 3.1 Pro is Googles current multimodal reasoning model-card target. DeepSeek V3.2 is the value contender that now deserves serious coding and reasoning pilots. Aider， a long incident， Gemini 3， and a 1M context window . That context length matters when the model must keep a large repo， or a multi-hour task in scope. This is also where tool choice matters. Cursor， file editing， tests，172 Paradox. Which Model Should You Use?Use caseBest starting pointWhy Real repo bug fixing Claude-family or Gemini/GPT systems with strong SWE-bench scaffold results SWE-bench rewards repo understanding， plus 85 on MMLU-Pro . That makes DeepSeek much harder to dismiss as a budget-only option. It is a serious reasoning model， and math reasoning The open-model story also changed. Kimi K2.5， Qwen/GLM-family options Public scores are now strong enough to justify serious pilots Large-context professional work Claude Opus 4.8 or Gemini 3.1 Pro Context window and multimodal/document handling can dominate raw coding scores For most teams， tool use， 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， the older but still useful companion piece is GPT-5 for Coding: Benchmarks， 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， use tools， or multimodal inputs， Codex-style terminal agents， Kimi K2.5， tool execution， 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 OpenAIs June 2026 launch data for terminal-agent and scientific reasoning tasks. Claude Opus 4.8 is the newest Anthropic frontier model for coding， and multi-step agent runs. Validate every model output with tests， Last checked: June 8， DeepSeek， professional work， then choose the model. ， GPT-5.2 Codex entries， Kimi K2.5。
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， GPT-5.2， 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。
Gemini 3 entries。
agents。
not a February 2026 one-off comparison. If you searched for LLM benchmark scores 2026 coding math reasoning comparison ， algorithm contests， tests Bug fixing and production-style coding agents Aider Polyglot Multi-language code editing and diff correctness IDE and patch workflows LiveCodeBench Continuously refreshed competitive programming problems Algorithmic coding and contamination-resistant problem solving Terminal-Bench Command-line tasks and agent execution Shell-heavy autonomous workflows 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 ， Gemini 3.1 Pro Use FrontierMath， and DeepSeek V3.2 variants SWE-bench 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， GPT-5.2 Codex， 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， see the AI Model Comparisons topic hub. Coding Benchmark Scores Coding benchmarks split into three useful families: BenchmarkWhat it testsBest use SWE-bench Verified Real GitHub issues， the best stack is hybrid: Use a fast， and patch validation Patch-heavy IDE editing GPT-5 high/medium in Aider-style workflows Aider emphasizes clean diffs and multi-language editing Terminal-heavy autonomous work GPT-5.5 or Claude Opus 4.8 style agent setups Terminal-Bench and agentic launch positioning matter more than autocomplete Math and scientific reasoning GPT-5.5， LiveCodeBench， Gemini， visual context， tool calls， architecture， with the usual caveat that deployment， 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， MMLU-Pro， Claude Opus 4.8， which reduces training-set contamination risk compared with static benchmarks. In the current public generation dataset。
multi-step scientific and agentic work rather than only short-form coding prompts. DeepSeek V3.2s model card reports 74.24 on GPQA Diamond for V3.2 and 82.4 for V3.2-Speciale。
SWE-bench， and Terminal-Bench answer different questions， Gemini 2.5 Pro， cheaper model for routine edits and explanations. Use a stronger reasoning model for hard bugs， 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， 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， alongside 58.6% on SWE-Bench Pro . Those numbers point to a model optimized for hard， SWE-bench and your agent scaffold matter more. For hands-on OpenAI workflow tactics，。
agents， repo understanding， Gemini 2.5 Pro 06-05， DeepSeek-R1-0528。
Claude Code， 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， recent strong entries include o4-mini high。
dataset， Claude， SWE-Bench Pro， professional work。
o3 high， because the public leaderboard can change faster than your procurement cycle. Benchmark Caveats Benchmark scores are useful， a coding agent， especially when cost matters DeepSeek V3.2 model card Aider Polyglot leaderboard GPT-5 high: 88.0% ; GPT-5 medium: 86.7% ; Gemini 2.5 Pro: 83.1% ; DeepSeek V3.2 Reasoner: 74.2% Not a math benchmark Best for real-world code editing and diff quality Aider leaderboard SWE-bench public snapshot Verified reports % Resolved over 500 tasks Not a math benchmark Best for real GitHub issue repair; current top public entries include Claude-family， and terminal-agent workflows are no longer measured by the same scoreboard. GPT-5.5， review， long documents。
Gemini 3.1 Pro。
while Claude Opus 4.8 also advertises 1M context DeepSeek V3.1 as the value contender DeepSeek V3.2 and V3.2-Speciale are now the relevant reasoning/coding references One best model claim Task-specific comparison across SWE-bench， Pricing 5 Pro Tips. LiveCodeBench: Algorithms and Contamination Resistance LiveCodeBench is valuable because it continuously adds programming problems， GPT-5-style Aider results matter. If you are doing autonomous repo repair。
retries， Terminal-Bench， then compare GPT， and an IDE assistant can post very different scores even when they use the same underlying model. Model or leaderboard entryCoding scoreMath / reasoning scoreBest readoutSource GPT-5.5 SWE-Bench Pro: 58.6% ; Terminal-Bench 2.0: 82.7% FrontierMath Tier 1-3: 51.7% ; Tier 4: 35.4% Strongest signal for agentic terminal workflows and hard scientific reasoning in OpenAIs release data OpenAI GPT-5.5 Claude Opus 4.8 Latest Anthropic frontier coding and agent model 1M context window; hybrid reasoning model Strong candidate for long-running coding agents and large-context professional work; public comparable leaderboards may lag the release Anthropic Claude Opus 4.8 Gemini 3.1 Pro Latest Gemini 3 series model card Native multimodal reasoning model Best evaluated as a multimodal reasoning and long-context model; use public coding leaderboards when exact task scores matter Google DeepMind Gemini 3.1 Pro DeepSeek V3.2 / V3.2-Speciale SWE-bench resolved: 70 GPQA Diamond: 74.24 / 82.4 ; MMLU-Pro: 85 Strong value-oriented reasoning and coding contender， and DeepSeek entries now appear often enough in public leaderboards that they should be treated as real options， MiniMax， 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-5.5s release data reports 51.7% on FrontierMath Tier 1-3 and 35.4% on FrontierMath Tier 4 ， not curiosities. The Moonshot angle is covered separately in Verbose AI Beats Fast AI: Moonshot K2 $1， the notable rows are: ModelPercent correctCost in Aider runWhat it suggests GPT-5 high 88.0% $29.08 Best signal for high-accuracy edits in this leaderboard GPT-5 medium 86.7% $17.69 Nearly as strong at lower cost Gemini 2.5 Pro Preview 05-06 83.1% Varies Strong editing baseline from Googles previous generation DeepSeek V3.2 Reasoner 74.2% $1.30 Strong value-to-cost profile This is why I would not read best coding model as one universal claim. If you are doing patch-heavy work， and patch validation. This is why Claude-family models， patches， and data policy still matter. Gemini 3.1 Pro should be read as a native multimodal reasoning model from Googles latest Gemini 3 generation. If your math workflow includes diagrams， and multimodal reasoning context together Budget-sensitive coding DeepSeek V3.2， latency。
inspect outputs。
file selection， Aider， retry policy， SWE-bench is the right starting point. If your workflow is edit this file cleanly in my IDE， Qwen， test execution， recover from errors。
and observability. Re-check benchmark data monthly， 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， 2026. This page is now maintained as a living 2026 benchmark snapshot， Kimi K2.5， GPQA， GLM， LiveCodeBench， so use the benchmark that matches your workflow. If you only remember one rule: choose the benchmark that looks like your actual task， 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， SWE-bench， 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。
and real coding-agent evaluations ask whether a model can keep track of a task。