Grok 4.6 vs Opus 5 vs GPT 5.6 Sol: Best agentic AI models

TL;DR

• Claude Opus 5 has the strongest broad agentic benchmark position: 63 on the current Artificial Analysis Intelligence Index at max effort. It also scores 79.2% on SWE-bench Pro, 68.8% on DeepSWE v1.1, 70.6% on OSWorld 2.0, and 70.0% on CursorBench 3.2 at max effort. Use it for workloads that prioritize maximum task quality, with latency and token volume budgeted accordingly.
• Grok 4.6 is the price-performance leader for the current CursorBench 3.2 run: 70.8% at Extra High for $2.81 per task, ahead of Opus 5 Max at 70.0% for $8.23 and GPT-5.6 Sol Max at 67.2% for $5.69. It scores 61 on the Artificial Analysis Intelligence Index at High effort, tied with GPT-5.6 Sol Max and two points behind Opus 5 Max.
• GPT-5.6 Sol remains an excellent platform choice for teams building around the Responses API: OpenAI reports 72.7% on DeepSWE v1.1, 88.8% on Terminal-Bench 2.1, 90.4% on BrowseComp, and 62.6% on OSWorld 2.0. Its published CursorBench 3.2 result is third among these three.

The best choice depends on the job. Pick Opus 5 for the broadest high-end benchmark strength, Grok 4.6 for current CursorBench efficiency, and GPT-5.6 Sol when the OpenAI tool stack is part of the architecture. Confirm the choice with a shared evaluation of your own workflow.

The current comparison in one table

Model and setting	Artificial Analysis Intelligence Index	CursorBench 3.2	Average cost per CursorBench task	Average steps	List API price per 1M input/output tokens
Claude Opus 5 Max	63	70.0%	$8.23	78	$5 / $25
Grok 4.6 Extra High	61 at High effort	70.8%	$2.81	46	$2 / $6
GPT-5.6 Sol Max	61	67.2%	$5.69	48	$5 / $30

CursorBench 3.2 is the cleanest current like-for-like comparison because it reports all three exact models in the same benchmark view. Cursor calculates its task cost from each model's published input, cache, and output prices and the tokens consumed during its benchmark runs. The site also warns that small score differences may not be statistically meaningful. Grok 4.6 Extra High's 70.8% lead over Opus 5 Max's 70.0% is therefore a meaningful purchasing signal, not a declaration of universal superiority.

The Artificial Analysis Intelligence Index is a composite index spanning nine evaluations, including GDPval-AA v2, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, and long-context reliability work. At the published settings, Opus 5 Max scores 63 and leads the three-model group. Grok 4.6 High and GPT-5.6 Sol Max each score 61. The index gives a broader capability signal than a coding-agent test; it does not replace a workflow evaluation.

Claude Opus 5: strengths and weaknesses

Opus 5 is the strongest all-around candidate when an agent must handle difficult code, knowledge work, tool use, computer use, and long-running multi-step tasks. Its 63 Artificial Analysis Intelligence Index score at max effort leads Grok 4.6 High and GPT-5.6 Sol Max by two points. Anthropic's system card reports 79.2% on SWE-bench Pro, 68.8% on DeepSWE v1.1, 53.4% on FrontierCode 1.1 Main, and 63.6% on FrontierCode 1.1 Extended. It also reports 70.6% on OSWorld 2.0, 90.8% on BrowseComp, 80.6% on Toolathlon-Verified, and 85.8% on MCP Atlas.

CursorBench shows the cost of operating at this level. Opus 5 Max scores 70.0%, costs $8.23 per task, consumes 61,838 tokens on average, and takes 78 steps. GPT-5.6 Sol Max scores 67.2%, costs $5.69 per task, and consumes 28,320 tokens. Grok 4.6 Extra High scores 70.8% at $2.81 per task.

Strengths

• Highest current Artificial Analysis Intelligence Index score of the three: 63 at max effort.
• Strong published engineering results: 79.2% on SWE-bench Pro and 68.8% on DeepSWE v1.1.
• Strong tool and computer-use evidence: 80.6% on Toolathlon-Verified, 85.8% on MCP Atlas, and 70.6% on OSWorld 2.0.
• A 1 million-token context window and $5/$25 input/output pricing.

Weaknesses

• CursorBench 3.2 costs $8.23 per task at Max, the highest of the three compared runs.
• The same run averages 78 steps and 61,838 tokens, which can increase latency and make cost less predictable for repeated customer tasks.
• Results change with effort setting, agent harness, tool policy, and safety fallbacks. Production evaluation must use the exact configuration that will ship.
• Its 70.0% CursorBench score trails Grok 4.6 Extra High by 0.8 points, a gap Cursor says may fall within normal evaluation variance.

Grok 4.6: strengths and weaknesses

Grok 4.6 is the strongest choice here for measured coding-agent efficiency. On CursorBench 3.2, Extra High achieves the highest listed score, 70.8%, at $2.81 per task and 46 steps. High effort still posts 69.9% at $2.34 per task and 39 steps. Those results are within 0.1 to 0.9 points of the top run while materially below the competing task costs.

Its broader current score is also frontier-level: Grok 4.6 High scores 61 on the Artificial Analysis Intelligence Index, tied with GPT-5.6 Sol Max. Reported model results also place Grok 4.6 at 65.9% on DeepSWE v1.1 and 61.3% on FrontierCode 1.1 Extended. The current $2 per million input tokens and $6 per million output tokens price gives it a significant token-price advantage over both competitors.

Strengths

• Best current CursorBench 3.2 result: 70.8% at Extra High.
• Lowest current CursorBench task cost among the three: $2.81 at Extra High and $2.34 at High.
• Fewer CursorBench steps than both alternatives: 46 at Extra High and 39 at High, versus 78 for Opus 5 Max and 48 for GPT-5.6 Sol Max.
• A 61 Artificial Analysis Intelligence Index score at High effort, tied with GPT-5.6 Sol Max.
• $2/$6 per-million-token list pricing.

Weaknesses

• Grok 4.6 High trails Opus 5 Max by two points on the current Artificial Analysis Intelligence Index.
• Its headline CursorBench lead over Opus 5 is narrow. Use a confidence interval or repeated runs before treating 70.8% versus 70.0% as decisive.
• Published benchmark coverage is currently thinner and less centralized than the documentation available for Opus 5 and GPT-5.6 Sol. Confirm deployment terms, tool behavior, rate limits, data handling, and regional availability against the current xAI documentation before procurement.
• A low price per task has to survive your tool loop, retrieval volume, retries, and human review. CursorBench's task economics will not equal a SaaS workflow's economics.

GPT-5.6 Sol: strengths and weaknesses

GPT-5.6 Sol offers the deepest published platform integration for teams using OpenAI's Responses API. OpenAI lists a 1.05 million-token context window, structured outputs, function calling, and hosted capabilities including web search, file search, code interpreter, hosted shell, computer use, MCP, and tool search. Its API list price is $5 per million input tokens, $30 per million output tokens, and $0.50 per million cached input tokens.

The current benchmark record is strong. OpenAI reports 72.7% on DeepSWE v1.1, 88.8% on Terminal-Bench 2.1, 64.6% on SWE-bench Pro, 90.4% on BrowseComp, and 62.6% on OSWorld 2.0. Sol Max scores 61 on the Artificial Analysis Intelligence Index, tied with Grok 4.6 High. In CursorBench 3.2, Sol Max posts 67.2%, costs $5.69 per task, uses 28,320 tokens, and takes 48 steps.

Strengths

• Strong published coding and tool-use results: 72.7% on DeepSWE v1.1, 88.8% on Terminal-Bench 2.1, 90.4% on BrowseComp, and 62.6% on OSWorld 2.0.
• The lightest CursorBench token use of the three compared Max runs: 28,320 tokens per task.
• A mature hosted-tool surface for teams that benefit from OpenAI-managed search, file, coding, shell, computer-use, and MCP capabilities.
• A 1.05 million-token context window and a 90% cached-input discount.

Weaknesses

• Its 67.2% CursorBench 3.2 score trails Opus 5 Max by 2.8 points and Grok 4.6 Extra High by 3.6 points.
• Its $5.69 CursorBench task cost sits above Grok 4.6 Extra High's $2.81 despite using fewer tokens, showing why token price alone cannot determine model economics.
• The $30 per-million output-token list price is higher than Opus 5's $25 and Grok 4.6's $6.
• Hosted tools reduce application infrastructure work, while authorization, tenant isolation, approval policy, idempotency, and post-write verification remain your application's responsibility.

How to choose for an embedded agent

Use the public results to form a shortlist, then run each candidate against the same bounded task. A useful test case includes the trigger, records in scope, allowed actions, approval boundary, expected final state, and cases where the agent must stop or escalate. Include denied access, ambiguous record matches, incomplete data, tool timeouts, rejected approvals, and tasks that are already complete.

Measure verified completion, wrong reads and writes, recovery after a failed tool call, human review time, latency, and total cost per accepted result. Keep model selection behind a stable workflow interface so you can change providers without rewriting product authorization or customer experience.

At Ginger Labs, we build embedded agents inside SaaS and web products. They work in side panels, inline surfaces, or modals and can progress defined multi-step work using a customer's schemas, stages, records, and data. The customer retains ownership of its API, data model, domain rules, user permissions, tenant boundaries, permitted actions, and definition of a correct result. Our SDK includes retrieval, evaluations, self-learning loops, and observability for this kind of model comparison.

Conclusion

Claude Opus 5 is the best current overall choice when quality across a broad set of agentic tasks carries the most weight. It leads this group on the Artificial Analysis Intelligence Index and has the deepest published results across software engineering, computer use, tool use, and knowledge work.

Grok 4.6 is the best current choice for coding-agent price-performance. It leads CursorBench 3.2 at Extra High effort, costs roughly one-third of Opus 5 Max per CursorBench task, and uses substantially fewer steps. Teams with throughput-sensitive, tool-heavy engineering tasks should put it at the top of the evaluation list.

GPT-5.6 Sol is the best fit when its hosted tool stack and Responses API are central to the product architecture. Its Terminal-Bench, DeepSWE, BrowseComp, and OSWorld results remain strong, while CursorBench indicates that its advantage lies less in the top coding-agent score and more in platform fit and lower token use.

Run the three models against the same customer workflow. Select the one that reaches the correct verified state at an acceptable cost and failure rate.

Sources

• CursorBench 3.2, Cursor. Accessed August 14, 2026.
• Claude Opus 5 model profile and Artificial Analysis methodology, Artificial Analysis. Accessed August 14, 2026.
• Introducing Claude Opus 5, Anthropic, July 24, 2026. Accessed August 14, 2026.
• Claude Opus 5 benchmark record, BenchLM, citing Anthropic's Opus 5 system card and CursorBench. Accessed August 14, 2026.
• GPT-5.6 launch evaluation table, OpenAI, July 9, 2026. Accessed August 14, 2026.
• GPT-5.6 Sol API model page, OpenAI. Accessed August 14, 2026.