GPT-6 Sol vs Claude Sonnet 5: Mid-Tier Model Routing at Identical $2/$10 Pricing

GPT-6 Sol launched on September 22, 2026, at $2 per million input tokens and $10 per million output tokens — the exact same price Anthropic charges for Claude Sonnet 5 (made permanent on August 10, after a planned hike to $3/$15 was scrapped). For the first time, two competing mid-tier frontier models from OpenAI and Anthropic sit at perfect price parity.

When cost is identical, the routing decision comes down to which model handles your workload better. We compared GPT-6 Sol and Claude Sonnet 5 across coding, agentic tasks, reasoning, computer use, factual reliability, and production characteristics — then built routing configs that pick the stronger model per task type.

Sources: OpenAI API Pricing, retrieved 2026-09-24; Anthropic Pricing, retrieved 2026-09-24; BenchLM GPT-6 Sol, retrieved 2026-09-24; BenchLM Claude Sonnet 5, retrieved 2026-09-24; GPT-6 Sol and Luna Benchmarks Explained, retrieved 2026-09-24; Introducing Claude Sonnet 5, retrieved 2026-09-24.

TL;DR — At a Glance

Pricing: Identical — With Important Differences in the Details

Both models charge $2/$10 at the headline level, and both offer 90% cache discounts ($0.20 per million cached input tokens). The similarities end there.

Long-context surcharge. GPT-6 Sol doubles its prices above 272K tokens: $4 input and $15 output per million tokens in the long-context tier. Sonnet 5 charges a flat $2/$10 regardless of context length up to its full 1M window.

Cache architecture. OpenAI's GPT-6 Sol preserves cache hits across reasoning-effort changes and tool toggles within a conversation — a significant improvement over GPT-5.6 where adjusting reasoning_effort would bust the cache. Anthropic's prompt caching offers two tiers: 5-minute ephemeral writes ($2.50/MTok) and 1-hour extended writes ($4/MTok), with cache hits at $0.20/MTok on both.

Batch pricing. Both offer a 50% discount on batch: GPT-6 Sol at $1/$5, Sonnet 5 at $1/$5. OpenAI Batch API returns results within 24 hours; Anthropic's Message Batches API targets completion within 24 hours as well.

Output cap. Sonnet 5 supports 128K max output tokens; GPT-6 Sol supports up to 100K. For large structured generations (full codebases, long reports), Sonnet 5 has a 28% advantage in single-response output length.

For workloads that stay under 272K context, the per-token cost is literally identical. For workloads that routinely exceed 272K, Sonnet 5 is cheaper.

Coding Quality: Different Benchmarks, Different Strengths

Direct head-to-head comparison is complicated because OpenAI and Anthropic use different primary coding benchmarks:

GPT-6 Sol coding highlights:

• DeepSWE v1.1: 68.8% (max effort) — within 1.1 points of Claude Fable 5's top score (69.9%), at an estimated 80% lower cost per task
• FrontierCode: Matches Claude Fable 5.1 at extra-high effort on production-ready PR quality

Claude Sonnet 5 coding highlights:

• SWE-bench Verified: 85.2% — resolving real open-source issues with test-driven validation
• SWE-bench Pro: 63.2% — harder, multi-file software bugs
• Terminal-Bench 2.1: 80.4% — complex terminal-based development workflows
• CursorBench 3.2: 61.5% — AI-assisted editing performance in IDE contexts

Sonnet 5 has broader and deeper coding benchmark coverage (13 coding benchmarks on BenchLM vs 2 for Sol). It excels at the sustained, multi-step coding workflows that matter in production — investigating bugs, writing reproducing tests, and verifying fixes in a single pass.

Sol, on the other hand, focuses on cost-efficient performance: it captures approximately 90–95% of its flagship sibling Astra's capability at 20% of the cost per task.

Routing recommendation: For coding-agent workloads (Claude Code, Cursor, Codex), Sonnet 5's deeper coding benchmark coverage and higher SWE-bench scores make it the stronger default. Route to Sol when you need enterprise-grade automation (AutomationBench workloads) or when you're running high-volume coding triage where cost-per-task matters more than peak accuracy.

Agentic Capabilities: Enterprise vs Desktop

GPT-6 Sol on enterprise automation:

• AutomationBench 1.0.6: 33.2% (xhigh effort) — outpaces Claude Opus 5 at maximum effort (26.9%) while costing $0.27 per task (8.9× cheaper than Fable 5.1)
• Agents' Last Exam: 56.4% — autonomous workflows across 55 sub-industries, 95% of Astra's accuracy at a fraction of the cost

Claude Sonnet 5 on desktop/browser agents:

• OSWorld-Verified: 81.2% — GUI navigation and OS-level tasks
• BrowseComp: 84.7% — browser automation
• HLE with tools: 57.4% — tool-augmented knowledge work
• ApprenticeBench: 16% — long-horizon apprentice-level tasks

Sol wins on cross-application enterprise automation (API orchestration, CRM workflows, multi-tool pipelines). Sonnet 5 wins on desktop and browser agent tasks (GUI navigation, screen automation, complex web interactions).

Reasoning and Knowledge

GPT-6 Sol:

• BenchLM overall: 82.17/100 (rank #4)
• AA-LCR reasoning: 83.7%
• AA-MMMU-Pro multimodal: 83.3%
• AA-HLE knowledge: 47.9%
• Hallucination rate: ~50% fewer factual mistakes than GPT-5.6 Sol

Claude Sonnet 5:

• BenchLM overall: 67.25/100 (rank #19)
• AA-LCR reasoning: 82.0%
• AA-GPQA Diamond: 91.1%
• AA-HLE knowledge: 41.3%
• MMLU-Pro: 87.5%

Sol ranks significantly higher overall on BenchLM (82.17 vs 67.25), though both have partial benchmark coverage. Sol's factual reliability improvements are notable: OpenAI demonstrated ~50% fewer factual mistakes versus its predecessor, with the improvement coming from deeper internal verification rather than shorter responses.

Sonnet 5 leads on domain-specific expert knowledge (GPQA Diamond 91.1%) and professional knowledge benchmarks (MMLU-Pro 87.5%).

Context Window and Caching

Both models support million-token-scale context, but the cost implications diverge at scale. A 500K-token prompt on Sol incurs long-context pricing ($4/MTok input), while Sonnet 5 stays at the base $2/MTok. For code-agent workflows that feed entire repositories into context, this difference adds up.

OpenAI's cache-across-effort improvement is significant for agentic loops where reasoning effort varies between turns — a pattern common in coding agents that shift between high-effort debugging and low-effort file-listing.

Multimodal and Tool Calling

Both models accept text and image input. Neither supports audio or video input natively at the API level.

Tool calling: Both support parallel tool calling. GPT-6 Sol adds programmatic tool calling (introduced with GPT-5.6) where the model can execute tools in a deterministic sequence without additional round trips. Sonnet 5's tool calling follows Anthropic's standard tool-use protocol with input schema validation.

For agentic workflows with complex tool chains, Sol's programmatic tool calling can reduce latency by eliminating round trips, while Sonnet 5's approach is simpler to integrate with existing OpenAI-compatible tooling.

Decision Matrix: Pick Sol If X, Sonnet 5 If Y

TheRouter Config: Cross-Provider Routing at Price Parity

Because both models cost the same, you can route between them based purely on task fitness without worrying about cost deltas. A routing configuration that selects the stronger model per workload type:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.therouter.ai/v1",
    api_key="your-therouter-key",
)

# Coding tasks → Sonnet 5 (stronger SWE-bench)
coding_response = client.chat.completions.create(
    model="anthropic/claude-sonnet-5",
    messages=[{"role": "user", "content": "Fix the race condition in this Go code..."}],
)

# Enterprise automation → Sol (stronger AutomationBench)
automation_response = client.chat.completions.create(
    model="openai/gpt-6-sol",
    messages=[{"role": "user", "content": "Draft a sales outreach email based on this CRM data..."}],
)

For fallback routing, set up Sonnet 5 → Sol (or vice versa) so that if one provider has an outage, your requests automatically route to the other at the same cost:

# Fallback: if Sonnet 5 is unavailable, route to Sol at same price
response = client.chat.completions.create(
    model="anthropic/claude-sonnet-5",
    extra_body={
        "route": {
            "fallback": ["openai/gpt-6-sol"]
        }
    },
    messages=[{"role": "user", "content": "Summarize this quarterly report..."}],
)

See model fallback routing for the full configuration reference.

When to Route to GPT-6 Luna ($0.10/$0.50) Instead

Not every task needs a $2/$10 model. GPT-6 Luna sits at $0.10/$0.50 — 20× cheaper than both Sol and Sonnet 5 — and still achieves:

• DeepSWE v1.1: 66.6% (max effort) — within 2.2 points of Sol
• AutomationBench: Luna at high effort improves 5.4 points over GPT-5.6 Luna at 58% lower cost per task
• Factual accuracy: Matches GPT-5.6 Sol at higher reasoning effort levels

For triage tasks (linting, syntax fixes, diff review, test generation), Luna at max effort can match or exceed Sol at medium effort. Route expensive multi-step tasks through Sol or Sonnet 5, and delegate mechanical sub-tasks to Luna.

FAQ

Are GPT-6 Sol and Claude Sonnet 5 really the same price?

Yes. Both charge $2 per million input tokens and $10 per million output tokens. Anthropic made Sonnet 5's introductory pricing permanent on August 10, 2026. OpenAI launched Sol at this price on September 22, 2026. The one difference: Sol's price doubles above 272K tokens, while Sonnet 5 stays flat across its full 1M context.

Which model is better for coding?

Sonnet 5 has stronger coding benchmark coverage: 85.2% on SWE-bench Verified, 63.2% on SWE-bench Pro, 80.4% on Terminal-Bench 2.1. Sol's DeepSWE 68.8% is competitive but tested on a different benchmark. For production coding agents (Cursor, Claude Code), Sonnet 5 is the safer pick.

Which model hallucinates less?

Sol. OpenAI reports approximately 50% fewer factual mistakes versus GPT-5.6 Sol, with the improvement verified through verbosity-controlled sweeps. Sonnet 5's hallucination rate is not directly comparable due to different evaluation methodologies.

Can I route between them with one API key?

Yes. TheRouter provides a single OpenAI-compatible API key that routes to both OpenAI and Anthropic models. You can configure per-request model selection, fallback chains, or workload-based routing through the same endpoint.

Should I still use GPT-6 Astra or Claude Opus 5.5?

If you need peak intelligence and cost is secondary, yes. Astra ($10/$50) ranks #1 on BenchLM and leads on nearly every frontier benchmark. Opus 5.5 ($4/$20) ranks #2 and dominates long-horizon coding tasks. Sol and Sonnet 5 target the tier where high-quality agentic work needs to be economically sustainable at scale.