Top 10 AI Models for Coding in October 2026: Best LLMs for Agentic Software Work

TL;DR

• As of October 5, 2026, Claude Opus 5.5 is the best AI model for coding, leading Vals’ independent Terminal-Bench 4.0 run at 65.15%, with Claude Sonnet 5.5 a statistical near-tie at 64.14%.
• GPT-6.1 Sol is the best LLM for coding on cost: 55.05% on Terminal-Bench 4.0 at $1.72 per task, against $13.20 for Opus 5.5 and $9.58 for GPT-6 Astra.
• SWE-bench Verified no longer separates frontier models: Vals archived it in September 2026 after seven models passed 95%, so this ranking uses Terminal-Bench 4.0 and Datacurve’s DeepSWE v1.1 instead.
• The best open-weight coding models are Z.ai’s GLM-5.3 (38.89% on Terminal-Bench 4.0, 69% on DeepSWE) and Xiaomi’s MiMo-V2.6-Pro (31.31% at $0.50 per task, MIT license).
• Harness matters: Claude Sonnet 5.5 scores 70.6% on Anthropic’s own Terminal-Bench 4.0 run and 64.14% on Vals’ run, so test any model inside the coding agent the team actually uses.

The best AI model for coding in October 2026 is the one that completes long, multi-step engineering tasks in a real repository or terminal, at a cost per task the team can sustain, inside the coding agent the team already runs. That definition has moved away from single-function code generation: today’s coding benchmarks hand the model a shell, a codebase, and a goal, then grade the committed result. This guide ranks the ten best LLMs for coding using independent agentic leaderboards as of October 5, 2026, plus price per task and notes on how each model behaves in Claude Code, Cursor, and Codex. For the general-purpose ranking of the same models, see the companion list of the top AI models in October 2026.

How the Coding Models Were Evaluated

Each model was scored on independent agentic coding results first, vendor-reported results second, and cost per completed task third. Harness compatibility (which coding agents can call the model and how it behaves there) decided close calls.

SWE-bench Verified is deliberately absent from the ranking. Vals archived its SWE-bench Verified board on September 1, 2026, with Claude Opus 5 at 97% and seven of 86 models at 95% or above. At that ceiling, the remaining differences say more about contamination and grading noise than about engineering skill.

Best LLMs for Coding Compared at a Glance

The table lists each model’s independent agentic scores, cost per Terminal-Bench 4.0 task as measured by Vals, and standard API prices. A dash means the leaderboard has not yet scored that model.

Two readings of this table are worth keeping. Terminal-Bench 4.0 is hard: the leader fails about a third of tasks, and only eleven of thirty models evaluated by Vals clear 30%. And cost per task is a function of tokens used, not list price, which is why Sonnet 5.5 costs more per task than Opus 5.5 despite half the per-token price.

1. Claude Opus 5.5

Claude Opus 5.5 is the best AI model for coding as of October 5, 2026. Anthropic released it on September 22 as an upgrade to Opus 5 for long-running agentic coding and knowledge work.

• Benchmarks: 65.15% on Vals’ Terminal-Bench 4.0 (first of 43 models). Anthropic reports 66.4% on Terminal-Bench 4.0, 57.8% on CursorBench 4.0, and 54.4% on FrontierCode 1.1, all ahead of Claude Fable 5.1. It also leads Artificial Analysis’s general index at 58.
• Price and context: $4 / $20 per million tokens, cache reads $0.20, 1M context, 128K output. A fast mode costs $8 / $40.
• In coding agents: Native in Claude Code. Anthropic says it solved more terminal tasks than Opus 5 in less than half the steps, per the Opus 5.5 launch post. Custom harnesses need updates: thinking is always on, forced tool use now returns an error, and text between tool calls comes back inside thinking blocks.
• Weaknesses: $13.20 per Terminal-Bench task is mid-pack on cost, and the breaking API changes affect teams migrating homegrown agents from Opus 5.

Best for: Hard, ambiguous engineering work (large refactors, debugging across services, long autonomous sessions) where a failed attempt costs more than the tokens.

2. Claude Sonnet 5.5

Claude Sonnet 5.5 is effectively tied with Opus 5.5 on terminal tasks and is the default choice for everyday coding in Anthropic’s lineup. It shipped on September 28, 2026.

• Benchmarks: 64.14% on Vals’ Terminal-Bench 4.0, one point behind Opus 5.5. Anthropic reports 70.6% on its own Terminal-Bench 4.0 run (ahead of Opus 5.5’s 66.4% there), 55.5% on CursorBench 4.0, and 46.2% on FrontierCode 1.1, up from 10.3% on Terminal-Bench 4.0 for Sonnet 5.
• Price and context: $2 / $10 per million tokens, 1M context.
• In coding agents: Available in Claude Code and the Claude API as claude-sonnet-5-5. Anthropic’s Sonnet 5.5 announcement positions it for “well-scoped everyday tasks” and bug fixing, with Opus 5.5 for work that needs more judgment. Teams that ran Sonnet 5 with thinking off must switch to the new between_tools setting.
• Weaknesses: Artificial Analysis measured about 193K output tokens per task at max effort, the highest it has recorded, which is why its cost per Terminal-Bench task ($16.51) exceeds Opus 5.5’s. Lower effort settings recover most of the price advantage.

Best for: Daily feature work, bug fixes, and test writing at high volume, with effort tuned down for routine requests.

3. GPT-6 Astra

GPT-6 Astra is OpenAI’s strongest coding model and the most token-efficient frontier model on this list. It launched on September 3, 2026.

• Benchmarks: 59.60% on Vals’ Terminal-Bench 4.0 (third) and 74% on DeepSWE v1.1 (tied first on Datacurve’s board). Artificial Analysis scored it 62 on its Coding Agent Index, tied with Claude Fable 5.1 at the time.
• Price and context: $10 / $50 per million tokens, cached input $1, 1.05M context, 128K output. Inputs over 272K tokens are billed at higher rates.
• In coding agents: Available in Codex and the OpenAI API. Artificial Analysis found it uses about 27K output tokens per task and about 24 reasoning turns, against roughly 60 for competing models, which keeps its cost per task ($9.58) below Opus 5.5’s despite a 2.5x higher list price. The GPT-6 Astra launch post also notes it is the first OpenAI model rated Critical for cybersecurity, so some security-research requests are gated behind trusted access.
• Weaknesses: The highest per-token price here, and long-context requests above 272K tokens get expensive quickly.

Best for: Large-repository agents in Codex where decisive, low-turn execution and long context matter more than list price.

4. GPT-6.1 Sol

GPT-6.1 Sol is the best LLM for coding on price-performance in October 2026. OpenAI released it at DevDay on September 29, 2026, one week after GPT-6 Sol, which it replaces.

• Benchmarks: 55.05% on Vals’ Terminal-Bench 4.0 at $1.72 per task, the lowest cost per task among models above 50%. 96.89% on Vals’ IOI run and 65.12% on Vals’ Code Migration benchmark. OpenAI reports that it matches GPT-6 Astra on DeepSWE v1.1 at about one-fifth of the cost.
• Price and context: $2 / $10 per million tokens, cached input $0.10, 1.05M context, 128K output.
• In coding agents: Reported as the new default coding model in Codex, and it adds beta multi-agent delegation in the Responses API, per the GPT-6.1 Sol launch post. It does not support the none reasoning effort, so latency-sensitive autocomplete paths need a different model.
• Weaknesses: Ten points behind the Anthropic pair on Terminal-Bench 4.0, and it uses 10% to 30% more output tokens than GPT-6 Sol.

Best for: CI pipelines, batch code migration, and high-volume agent fleets where cost per merged change is the metric.

5. Gemini 4 Argon

Gemini 4 Argon is Google’s new frontier model and posts the highest vendor-reported long-horizon coding score, but access is still restricted. Google announced it on September 30, 2026, and is rolling it out first through the Fairwind Program for cyber defenders.

• Benchmarks: 57.58% on Vals’ Terminal-Bench 4.0 and first on the Vals Index (68.90%). Google reports 77.9% on DeepSWE v1.1, ahead of Opus 5.5 and GPT-6 Astra on that test, and 68% on CWE-bench v1 for vulnerability remediation.
• Price and context: Introductory $2 / $10 per million tokens, rising to $4 / $20; cached input at 95% off; 1M context.
• In coding agents: Not generally available in coding tools as of October 5. Google cites internal use for large migrations, including an 800,000-line rewrite of a kernel codebase to Rust, in the Gemini 4 Argon announcement.
• Weaknesses: Limited access, and its $17.64 cost per Terminal-Bench task is among the highest on Vals’ board.

Best for: Large-scale code migration and security remediation, once access widens beyond trusted programs.

6. Claude Fable 5.1

Claude Fable 5.1 is Anthropic’s top tier, released September 1, 2026, and it remains a strong long-session coding model, though Opus 5.5 now beats it on Anthropic’s own coding tables.

• Benchmarks: 58.08% on Vals’ Terminal-Bench 4.0. Anthropic’s Opus 5.5 post lists Fable 5.1 at 55.8% on Terminal-Bench 4.0 and 51.8% on CursorBench 4.0.
• Price and context: $10 / $50 per million tokens, cache reads $0.25 (a 75% cut from Fable 5), 1M context, 128K output.
• In coding agents: Available in Claude Code and on the Claude API, Bedrock, Google Cloud, and Microsoft Foundry. The Fable 5.1 launch post targets long-running agentic coding; Anthropic’s docs rate its latency as slower than Opus 5.5.
• Weaknesses: $17.18 per Terminal-Bench task for a lower score than Opus 5.5.

Best for: Teams already running Fable for multi-hour research-and-code sessions that have not yet re-evaluated against Opus 5.5.

7. GLM-5.3

GLM-5.3 is the best open-weight model for coding on independent terminal benchmarks. Z.ai released it on August 14, 2026, and published weights on Hugging Face under a custom GLM-5.3 license.

• Benchmarks: 38.89% on Vals’ Terminal-Bench 4.0 (tenth overall, first among open-weight models) and 69% on DeepSWE v1.1. Z.ai reports DeepSWE improving from 46.2 to 66.9 over its predecessor.
• Price and context: $1.40 / $4.40 per million tokens, cached input $0.26, 1M context, 128K output.
• In coding agents: Z.ai’s API speaks the OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages protocols, per the GLM-5.3 developer docs, so it can be pointed at from Claude Code or Codex-style tools by changing the base URL. A GLM Coding Plan bills by points, with off-peak calls at half rate.
• Weaknesses: The license is not MIT, unlike GLM-5.2, and a $9.37 cost per Terminal-Bench task is high for an open model because of long reasoning traces.

Best for: Teams that want an open-weight coding model they can self-host or call cheaply, with drop-in compatibility for Anthropic-protocol agents.

8. Gemini 3.8 Flash

Gemini 3.8 Flash is the strongest low-cost model on long-horizon repository tasks, released September 2, 2026.

• Benchmarks: 74% on DeepSWE v1.1, tied for first with GPT-6 Astra and Claude Opus 5 on Datacurve’s board. It scores only 19.19% on Vals’ Terminal-Bench 4.0, so its strength is concentrated in repository editing rather than open-ended terminal work.
• Price and context: $0.75 / $3.75 per million tokens through December 31, 2026, then $1.50 / $7.50. 1M input context, 65K output.
• In coding agents: Available through the Gemini API and Google’s apps. Google says it executes extra reasoning steps and calls tools iteratively on complex tasks, per the Gemini 3.8 Flash announcement, which raised output tokens per task by about 30%.
• Weaknesses: The split between DeepSWE and Terminal-Bench results means it needs validation on the team’s own tasks, and the price doubles in January.

Best for: Budget repository agents and code-review bots that edit files within a known codebase.

9. MiMo-V2.6-Pro

MiMo-V2.6-Pro is the cheapest model per completed agentic coding task on this list. Xiaomi released it on September 22, 2026, with weights under the MIT license.

• Benchmarks: 31.31% on Vals’ Terminal-Bench 4.0 at $0.50 per task, one-twenty-sixth of Opus 5.5’s cost per task. 85.22% on Vals’ Vibe Code Bench v1.1. Xiaomi reports 71.9% on DeepSWE v1.1.
• Price and context: $0.43 / $0.87 per million tokens; 1M context; 1.02 trillion total and 42 billion active parameters.
• In coding agents: Available through Xiaomi’s API, OpenRouter, and Xiaomi’s own MiMo Code tool, with weights on the MiMo-V2.6 Hugging Face collection.
• Weaknesses: About 41 output tokens per second on Xiaomi’s API, which makes interactive sessions slow, and weak scores on Vals’ Code Migration (43.01%) and IOI runs.

Best for: Background agents, test generation, and high-volume batch work where latency is acceptable and cost per task dominates.

10. Grok 4.7

Grok 4.7 is SpaceXAI’s coding and knowledge-work model, released September 21, 2026, and it ships with first-party integration in Cursor.

• Benchmarks: 28.79% on Vals’ Terminal-Bench 4.0. Artificial Analysis measured 73% on DeepSWE v1.1 (up from 65%) and 56 on its Coding Agent Index (up from 47). SpaceXAI says it sits at the price-performance frontier on CursorBench 4.0.
• Price and context: $2 / $6 per million tokens, cache hits $0.50, 500K context.
• In coding agents: Available in Cursor and SpaceXAI’s Grok Build agent, with a faster variant offered only in those two tools at twice the standard rate, per the Grok 4.7 announcement.
• Weaknesses: Output token usage rose 125% over Grok 4.6, which pushes its Terminal-Bench cost to $18.09 per task, and the 500K context is half that of its peers.

Best for: Cursor-centric teams that want a low output price for interactive editing.

Also worth testing: Kimi K3 (69% on DeepSWE v1.1, open weights under a custom license, $3 / $15), DeepSeek V4.1 Flash (MIT license, 19.70% on Terminal-Bench 4.0 at $0.50 per task), and Meta’s Muse Spark 1.3 (24.75% on Terminal-Bench 4.0 at max effort). The wider open-weight picture is covered in the analysis of China’s open-weight models.

How These Models Behave in Claude Code, Cursor, and Codex

The coding agent wrapped around a model changes results as much as the model choice does. A harness decides how much context the model sees, how tool calls are structured, when tests run, and when the agent stops, so a model’s leaderboard score is an upper bound for some tools and an underestimate for others.

• Claude Code: Anthropic models are native, and Claude Code can also call other providers through an Anthropic-compatible endpoint. Opus 5.5 and Sonnet 5.5 are the strongest choices here; GLM-5.3 works through its Anthropic Messages endpoint. The comparison of Claude Code gateways covers per-developer budgets and routing for this setup.
• Cursor: Cursor ships many providers. Anthropic’s CursorBench 4.0 results put Opus 5.5 (57.8%) and Sonnet 5.5 (55.5%) ahead of Fable 5.1 (51.8%), and Grok 4.7 has a Cursor-exclusive fast mode.
• Codex: OpenAI’s agent runs GPT-6 Astra and GPT-6.1 Sol, and Codex CLI can reach other providers through an OpenAI-compatible base URL. Astra’s low turn count suits long cloud tasks; 6.1 Sol suits high-volume use.

For a tool-by-tool comparison of the agents themselves, see the roundup of AI coding agents. Task length is the other variable: METR’s measurements show the length of software tasks frontier agents can finish has grown roughly 10x a year, as explained in the piece on METR time horizons, so a model that looks marginal on 20-minute tasks may separate clearly on multi-hour ones.

How Teams Access and Switch Between These Models

Engineering teams rarely standardize on a single coding model in October 2026. A common split is Opus 5.5 for hard tasks, Sonnet 5.5 or GPT-6.1 Sol for routine work, and an open-weight model for batch jobs, which only works if every coding agent can reach every model through one controlled endpoint.

Bifrost, an open-source AI gateway from Maxim AI, provides that endpoint. Used as an LLM gateway, it exposes 1,000+ models from 20+ providers behind OpenAI-, Anthropic-, and Gemini-compatible APIs.

That compatibility lets Claude Code, Codex CLI, and Cursor call models from other providers through a base-URL change. For a multi-model coding setup, the gateway layer covers four jobs:

• Routing and fallbacks: automatic fallbacks move a request to a second provider on errors or rate limits, so a provider incident does not stall every developer’s agent.
• Per-team budgets: governance controls assign virtual keys with budgets and rate limits per developer or team, which caps spend when an agent loops on a $10/$50 model.
• Cost and latency visibility: gateway observability records tokens, cost, and latency per request, turning cost per task into a number measured on the team’s own repositories rather than a benchmark estimate.
• Tools and policy: as an MCP gateway, Bifrost controls which MCP tools each coding agent can call, and guardrails apply content policies to agent traffic regardless of model.

Coding agents increasingly run on developer laptops outside any configured endpoint. Bifrost Edge, in early access, extends the same gateway governance and security (virtual keys, budgets, guardrails, audit logs) to Claude Code, Codex, Cursor, and their MCP servers on each machine, with endpoint enforcement on the device.

Frequently Asked Questions

What is the best AI model for coding right now?

Claude Opus 5.5 is the best AI model for coding as of October 5, 2026, with 65.15% on Vals’ independent Terminal-Bench 4.0 run. Claude Sonnet 5.5 is one point behind at half the per-token price. GPT-6.1 Sol is the best value, scoring 55.05% at $1.72 per task.

Is Claude or GPT better for coding?

On independent terminal benchmarks, Claude leads: Opus 5.5 and Sonnet 5.5 score 65.15% and 64.14% on Terminal-Bench 4.0, against 59.60% for GPT-6 Astra. GPT models are more token-efficient and cheaper per task; GPT-6.1 Sol costs $1.72 per task versus $13.20 for Opus 5.5. GPT-6 Astra also ties for first on DeepSWE v1.1.

What is the best open-source LLM for coding?

GLM-5.3 leads open-weight models on Terminal-Bench 4.0 (38.89%) and scores 69% on DeepSWE v1.1, under a custom license. For a permissive MIT license, MiMo-V2.6-Pro (31.31% at $0.50 per task) and DeepSeek V4.1 Flash are the strongest options. Kimi K3 also scores 69% on DeepSWE.

Which AI model is best in Cursor?

Anthropic’s CursorBench 4.0 results rank Claude Opus 5.5 (57.8%) and Sonnet 5.5 (55.5%) highest among models it reported. Grok 4.7 is the price-focused option, with a Cursor-exclusive fast variant. Results depend on the task mix, so compare two or three models on the team’s own repository.

Is SWE-bench still a good benchmark for coding models?

SWE-bench Verified no longer separates frontier models. Vals archived its leaderboard in September 2026, when seven of 86 models scored 95% or higher. Harder agentic tests are better guides: on Terminal-Bench 4.0, scores run from 0% to 65.15%, and the leader still fails about a third of tasks. DeepSWE v1.1 uses original tasks written to avoid contamination.

How much does an AI coding agent cost per task?

On Vals’ Terminal-Bench 4.0 run, cost per task ranges from $0.50 (MiMo-V2.6-Pro, DeepSeek V4.1 Flash) to about $18 (Grok 4.7, Gemini 4 Argon), with Opus 5.5 at $13.20 and GPT-6.1 Sol at $1.72. Token usage, not list price, drives most of the difference.

Choosing the Best AI Model for Coding

The best AI model for coding in October 2026 is Claude Opus 5.5 for difficult work, with Claude Sonnet 5.5 nearly level and GPT-6.1 Sol the clear choice when cost per task decides. GPT-6 Astra and Gemini 4 Argon lead on specific long-horizon tests, and GLM-5.3 and MiMo-V2.6-Pro bring open weights within reach of the closed tier. Since the leaderboard reorders roughly monthly, the practical move is to keep coding agents model-agnostic and measure cost per task on real repositories.