GPT-6.1 Sol vs Claude Sonnet 5.5 vs Grok 4.7 vs Muse Spark

Key takeaways

• Shortlist for coding / agentic work in Oct 2026: GPT-6.1 Sol, Claude Sonnet 5.5 (via Claude), Grok 4.7, Muse Spark 1.3.
• List-price floor: Muse Spark ($1.25/$4.25) → Grok ($2/$6) → Sol and Sonnet (~$2/$10). Real cost still tracks tokens per task and cache hits.
• No open weights in this set. If you need open weights, you are shopping a different aisle.
• Pick by stack gravity (OpenAI / Anthropic / SpaceXAI / Meta) and job, not by a single viral leaderboard.
• Deeper single-model notes: Sol review, Sonnet 5.5, Grok 4.7, Muse Spark 1.3.

Table of contents

1. Quick answer
2. Criteria table
3. API price comparison
4. Pick by job
5. Stack gravity
6. What we are not claiming
7. FAQ

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Quick answer

If you already live in OpenAI Work or Codex, default to GPT-6.1 Sol and escalate to Astra only when evals force it. If your team is on Claude Code, default to Sonnet 5.5 and keep Opus for the hard slice. If you want live X/web context plus Grok Build or Cursor, try Grok 4.7. If you are building on Meta’s API or Muse Code and care about Standard token price, start with Muse Spark 1.3.

For most product teams, the “best coding model” is the one that wins your harness inside the vendor stack you already pay for. Cross-vendor bake-offs matter; screenshot leaderboards usually do not.

Last verified: October 2026.

Criteria table

Criteria	GPT-6.1 Sol	Claude Sonnet 5.5	Grok 4.7	Muse Spark 1.3
Vendor	OpenAI	Anthropic	SpaceXAI (xAI)	Meta
Primary job	Agentic coding, computer use, pro work	Fast coding + knowledge work	Coding + knowledge work + live search	Agentic coding / multimodal API
Context (published)	Large (confirm per API mode)	1M in / 128K out	500K	~1M
Open weights	No	No	No	No
Computer use	Yes (API / Responses tools)	Strong via Claude Code / computer use surfaces	Via Grok Build / product agents	Via Muse Code + API tools
Consumer chat home	Work/Codex (not ordinary Chat at launch)	claude.ai	grok.com / X	Not the consumer Muse agent
Directory page	/tool/gpt-6-1-sol	/tool/claude	/tool/grok	/tool/muse-spark

API price comparison

Standard list prices teams actually quote (verify on publish day):

Model	Input / 1M	Output / 1M	Cached input signal	Watch-outs
Muse Spark 1.3 (Standard)	~$1.25	~$4.25	~$0.15 / 1M	Contributor tier is cheaper if you allow training use
Grok 4.7	~$2	~$6	~$0.50 / 1M (notes)	Jumps to ~$4/$12 above ~200K prompts; fast variant ~2×
GPT-6.1 Sol	~$2	~$10	~$0.10 / 1M	Long context higher; Fast 2×; Batch/Flex 50%
Claude Sonnet 5.5	~$2	~$10	~$0.20 / 1M cache read	Anthropic claims lower cost per task vs Sonnet 5 at same sticker

How to read this without fooling yourself:

1. Sticker ≠ bill. Sonnet can beat Sol on dollars if it finishes in fewer tokens.
2. Cache changes rankings. Sol’s ~$0.10 cached input is a real lever for long agent loops.
3. Long prompts punish Grok once you cross the ~200K band.
4. Contributor / training tiers (Meta) are a privacy trade, not free lunch.

Pick by job

If you want…	Start with	Escalate / avoid
Default coding agent in OpenAI stack	GPT-6.1 Sol	Astra when Sol fails evals (Sol vs Astra)
Default coding agent in Claude Code	Sonnet 5.5	Opus 5.5 on hard tasks
Live X + web context in the loop	Grok 4.7	Not if you need the cheapest $20 generalist chat
Lowest Standard Meta API price + Muse Code	Muse Spark 1.3	Not if you needed Meta Muse the consumer agent
Best polished long docs	Sonnet 5.5 (usually)	Confirm vs your style guide
Cheapest list-price tokens in this four	Muse Spark Standard	Re-check Contributor privacy terms
Computer-use heavy OpenAI agents	Sol first	Astra if quality gaps show

A simple way to think about it: pick the default in your stack, then keep one escape hatch model for failures. Dual-homing four vendors on day one is how bills and prompt drift explode.

Stack gravity

Most “model bake-offs” are actually account bake-offs.

Already paying for…	Path of least regret
ChatGPT Plus/Pro + Codex/Work	Sol now; Astra selective; Dots are a different product
Claude Pro + Claude Code	Sonnet 5.5 default; Opus selective
SuperGrok / Cursor with Grok	Grok 4.7
Meta Model API / Muse Code	Muse Spark 1.3

Cross-stack comparisons are still worth running quarterly. Just budget eng time for harness diffs (tools, memory, retries), not only model ids.

What we are not claiming

• We are not publishing a fake “Intelligence Index” winner for this page.
• Vendor charts (OpenAI, Anthropic, SpaceXAI, Meta, Artificial Analysis, CursorBench, etc.) are dated snapshots. Link out; do not copy scores into evergreen tables without a verification date.
• Personal agents (Dots, Meta Muse) are out of scope here. See which AI agent 2026.

If a competitor page ranks one model #1 with no harness details, treat it as marketing.

FAQ

Which is the best coding model in October 2026?

There is no single winner. GPT-6.1 Sol and Claude Sonnet 5.5 are the safest defaults for most coding agents. Grok 4.7 fits teams that want live X/web context and Grok Build/Cursor. Muse Spark fits Meta API / Muse Code stacks at a lower Standard token price.

Which coding API is cheapest among Sol, Sonnet 5.5, Grok 4.7, and Muse Spark?

On published Standard list prices, Muse Spark 1.3 is lowest at about $1.25/$4.25 per 1M. Grok 4.7 is about $2/$6 under 200K prompts. Sol and Sonnet 5.5 both list about $2/$10. Your real bill depends on caching, prompt length, and tokens per task.

GPT-6.1 Sol vs Claude Sonnet 5.5: which should I pick?

Pick Sol if you are deep in OpenAI Work/Codex/API and care about cheap cached input for agent loops. Pick Sonnet 5.5 if Claude Code and writing quality matter more, or if your evals already win on Anthropic.

Is Muse Spark open source?

No. Muse Spark is a proprietary Meta model on the Meta Model API and Muse Code. Do not confuse it with Llama open weights or with Meta Muse the consumer agent.

Should I trust vendor benchmark charts?

Use them as hypotheses, not purchasing proof. Run your own harness on your repos and tools. This guide does not invent scoreboards.

Do any of these replace OpenAI Dots or Meta Muse?

No. Those are agent products. This page compares coding and professional models you call from APIs and IDEs.