Claude Opus 5.5 vs GPT-6.1 Sol: Benchmarks, Pricing and Tests

If you’re weighing Claude Opus 5.5 vs GPT-6.1 Sol for coding and agent work, the benchmark tables make it look like a close race that Opus wins by a few points. Our runs didn’t show a quality gap at all: both models passed every task we gave them, and the real difference was the bill. Opus 5.5 cost 3 to 8 times more per finished task than GPT-6.1 Sol, partly because of its list price and partly because it writes more and counts more tokens for the same prompt.

Original content from computingforgeeks.com - post 179821

This comparison puts the published benchmarks, API pricing, context and effort limits side by side. Then it runs the same three single-shot coding prompts and two multi-step agent tasks on both models through one harness, judged on a real Ubuntu server with k3s, OpenTofu and shellcheck. You’ll see what each model produced, where they behaved differently and what a task actually costs. Every price, benchmark figure and test run below was checked in October 2026, and the raw results sit next to each table.

Claude Opus 5.5 vs GPT-6.1 Sol at a glance

For most API workloads we’d pick GPT-6.1 Sol, because it matched Opus 5.5 on every pass/fail check we ran at a fraction of the price. Opus 5.5 earns its premium in narrower cases. On most of the shared third-party rows below it leads Sol, by 10 to 27 percentage points on Vals AI’s coding runs, it keeps one price across its 1M-token context, and its code comes with more guard rails than you asked for. The table sums up what we measured and what the vendors publish.

Item	Claude Opus 5.5	GPT-6.1 Sol
API model ID	claude-opus-5-5	gpt-6.1-sol
Input / output price per 1M tokens	$4 / $20	$2 / $10
Cached input per 1M tokens	$0.20	$0.10
Context window	1M tokens, same price across it	1.05M tokens, 2x input and 1.5x output above 272K
Max output	128K tokens	128K tokens
Reasoning control	Adaptive thinking, always on, effort levels	reasoning.effort low to max, no none
Single-shot tasks passed (our lab)	18 of 18	18 of 18
Agent tasks passed (our lab)	6 of 6	6 of 6
Mean cost, Kubernetes agent task	$0.53	$0.063
Tokens counted for the same 166-token prompt	250	166

The last two rows decide most bills. A list price that is twice as high turns into a 3x gap on input once Anthropic’s API counts the same English prompt as 51% more tokens (its tokenizer plus chat framing). On agent loops the gap widens further, because Opus 5.5 also ran more tool calls and resent a longer history each turn, and you pay for that history every time. Prices come from the Anthropic pricing page and the GPT-6.1 Sol model page.

How we tested both models

Both models were called through OpenRouter’s chat completions endpoint with the provider pinned, so Opus 5.5 ran on Anthropic and GPT-6.1 Sol ran on OpenAI, never on a fallback host. That kept one request format, one tool-call schema and one usage report for both, which matters more than it sounds: a harness that speaks two different APIs ends up testing the glue code as much as the models. The pinned request looks like this, and you can run it as is with your own key and jq installed:

for m in anthropic/claude-opus-5.5 openai/gpt-6.1-sol; do p=${m%%/*}; echo "### $m"; curl -s https://openrouter.ai/api/v1/chat/completions -H "Authorization: Bearer $OPENROUTER_API_KEY" -H "Content-Type: application/json" -d '{"model":"'$m'","provider":{"only":["'$p'"],"allow_fallbacks":false},"usage":{"include":true},"messages":[{"role":"user","content":"Reply with the single word: ready"}]}' | jq '{model, provider, reply: .choices[0].message.content, usage: {prompt_tokens: .usage.prompt_tokens, completion_tokens: .usage.completion_tokens, reasoning_tokens: .usage.completion_tokens_details.reasoning_tokens, cost: .usage.cost}}'; done

Look at provider to confirm the pin held, and at prompt_tokens: the same six-word prompt costs 18 tokens on Opus 5.5 and 13 on GPT-6.1 Sol.

### anthropic/claude-opus-5.5
{
  "model": "anthropic/claude-opus-5.5",
  "provider": "Anthropic",
  "reply": "ready",
  "usage": {
    "prompt_tokens": 18,
    "completion_tokens": 4,
    "reasoning_tokens": 0,
    "cost": 0.000152
  }
}
### openai/gpt-6.1-sol
{
  "model": "openai/gpt-6.1-sol",
  "provider": "OpenAI",
  "reply": "ready",
  "usage": {
    "prompt_tokens": 13,
    "completion_tokens": 5,
    "reasoning_tokens": 0,
    "cost": 0.000076
  }
}

Every generated file and every agent command ran on one Ubuntu 26.04 LTS lab VM with 4 vCPUs and 8 GB of RAM. The judges and their versions:

PRETTY_NAME="Ubuntu 26.04.1 LTS"
k3s version v1.36.5+k3s1 (3dd98cc5)
OpenTofu v1.13.2
version: 0.11.0
v0.8.0
nginx version: nginx/1.28.3 (Ubuntu)

Those lines are Ubuntu, k3s, OpenTofu, shellcheck, kubeconform and nginx, in that order. The test plan had three parts:

• Single-shot coding: three prompts (a Kubernetes Deployment and Service, a PostgreSQL backup script, an AWS VPC in Terraform), 3 runs each, at the API default effort and again at high. That’s 36 files.
• Agent tasks: one run tool that executes a shell command on the VM, the same prompt for both models, 3 runs each on a broken nginx host and on a broken k3s namespace, graded afterwards by a script the models never saw.
• Speed: 3 streamed requests per model to time the first visible token and the output rate.

Costs below are list price times the token counts each API returned, and on every single-shot call they matched OpenRouter’s own cost field exactly. The agent-task costs use the same formula with no cache discount, and OpenAI caches long repeated prompts automatically, so treat Sol’s agent figures as an upper bound. The harness sent no Anthropic cache_control blocks either, so the Opus agent figures are uncached too and would drop with caching turned on. OpenRouter adds a small hop, so absolute latencies run slightly higher than a direct API call would.

1. Published benchmarks and where they disagree

Opus 5.5 leads most third-party leaderboards, but the size of the lead depends on who ran the test. Anthropic and OpenAI each publish numbers on harnesses they chose and overlap on only one or two benchmarks, so the most useful columns are the ones where Artificial Analysis or Vals AI ran both models the same way. These are the shared rows we could source:

Benchmark (who ran it)	Claude Opus 5.5	GPT-6.1 Sol
Terminal-Bench 4.0 (Vals AI)	65.2%	55.1%
Artificial Analysis Intelligence Index (max)	58.4	54.5
GDPval-AA Elo (Artificial Analysis, max)	1866	1608
SWE-bench Multimodal (Vals AI)	70.2%	43.5%
SRE Bench (Vals AI)	33.6%	50.8%
Terminal-Bench Science (Vals AI)	47.1%	52.9%
Terminal-Bench Science 0.1, cost per task at max effort (OpenAI)	$23.21	$5.47

Read the Terminal-Bench rows together. Anthropic’s own run puts Opus 5.5 at 66.4% on Terminal-Bench 4.0 at xhigh effort, and Vals AI measured 65.2% on its harness, so the vendor figure holds up there. Against Sol, the Vals run gives Opus a 10-point lead on the same benchmark. In this table, SRE Bench and Vals AI’s Terminal-Bench Science run are the two rows where Sol leads, by 17 and 5.7 points, and SRE Bench is the closest of these to the operations work in our lab. Don’t pick on a single leaderboard row.

Terminal-Bench Science also shows how far vendor numbers drift. Anthropic reports 58.7% for Opus 5.5 on its own run, Vals AI measured 47.1%, and OpenAI’s launch post puts Opus 5.5 at $23.21 per task at max effort against $5.47 for Sol, about four times as much. OpenAI also says Sol beats Opus 5.5 on GDP.pdf at less than half the cost per task, though OpenAI labels that Opus result “with fallbacks”.

Some headline numbers have no counterpart on the other side. Anthropic quotes FrontierCode, CursorBench 4.0 and OSWorld 2.1 for Opus 5.5; OpenAI quotes DeepSWE 1.1 and OSWorld 2.0 offline for Sol. The two OSWorld numbers use different versions of the suite, so don’t compare them. We could not find SWE-bench Verified, SWE-bench Pro or GPQA results for either model from a source that ran both, and the per-model detail lives in our Opus 5.5 release breakdown and the GPT-6.1 Sol, Terra and Luna tests.

2. API pricing and the real cost per task

On paper Opus 5.5 costs exactly twice as much as GPT-6.1 Sol: $4 against $2 per million input tokens and $20 against $10 per million output tokens. Cached input bills at 5% of the input rate on both, and both take 50% off for batch jobs. What the rate card does not show is how many tokens each model bills you for the same work. The three single-shot prompts are a clean test of that, because the inputs are identical text:

Task (mean of 3 runs, default effort)	Opus 5.5 input / output tokens	Sol input / output tokens	Opus 5.5 cost	Sol cost	Ratio
Kubernetes Deployment + Service	250 / 1,815	166 / 677	$0.0373	$0.0071	5.3x
PostgreSQL backup script	230 / 1,366	145 / 567	$0.0282	$0.0060	4.7x
Terraform VPC	246 / 894	181 / 598	$0.0189	$0.0063	3.0x
nginx agent task	18,829 / 1,535	14,421 / 662	$0.106	$0.036	3.0x
Kubernetes agent task	115,361 / 3,540	23,871 / 1,532	$0.532	$0.063	8.4x

Three things stack up here. Opus 5.5 counts 36% to 59% more input tokens for the same prompt, it writes roughly twice as much output (longer files plus more thinking), and on agent loops every extra turn resends the whole conversation. That is why the 2x list gap you see on the pricing pages becomes 3x to 5x on one-shot generation and 8x on the Kubernetes agent run, where Opus took 11 to 18 tool calls against Sol’s steady 7.

Long prompts shift the math the other way. GPT-6.1 Sol bills a request over 272K input tokens at twice the input rate and 1.5 times the output rate for the whole request, which takes it to $4 and $15. Opus 5.5 keeps $4 and $20 across its full 1M window, so for huge single prompts the per-token input rates meet. The same text still costs more on Opus because its API counts more tokens for it, and the 272K line is measured in Sol’s tokens.

If your agent works on a large repo, turn on prompt caching, because cache reads cost 5% of the input rate on both models and an agent resends the same files every turn. You can also trim what gets resent, and our guide to cutting Claude Code token usage covers the habits that keep the history short.

3. Single-shot coding: 36 files, 36 passes

Neither model wrote a single broken file. All 12 manifests passed kubeconform -strict, deployed on k3s with 3 Running pods and answered HTTP 200 through the Service. All 12 backup scripts came back clean from shellcheck -S info and failed loudly with exit 1 when DB_NAME was unset. Each one produced a valid gzip, uploaded once to the stubbed aws s3 cp, deleted a 10-day-old dump and kept a 3-day-old one. All 12 Terraform files passed tofu validate and tofu fmt -check. Raising effort to high changed nothing in those results, only the token count.

The two models wrote quite differently, though, and the differences repeated across all six runs of each:

• Opus 5.5 adds defensive extras: its backup scripts ran 47 to 72 lines with trap cleanup handlers, a dependency check and optional BACKUP_DIR and RETENTION_DAYS variables nobody asked for. Sol’s ran 17 to 24 lines and wrote to a fixed ./backups folder.
• Both hardened the pod spec: every manifest set a read-only root filesystem, dropped capabilities, blocked privilege escalation and used the RuntimeDefault seccomp profile. Opus also repeated runAsUser: 101 on the container, while Sol set it only at pod level.
• Both picked a stale AWS provider: Opus wrote ~> 5.0, which resolves to 5.100.0, and Sol pinned an exact = 6.0.0 or = 5.100.0. The registry’s newest release on test day was 6.71.0, so check the constraint before you apply either file.

The provider finding is the one that would bite you in production. Both configs validate, but an exact pin to 6.0.0 freezes you on a release from mid-2025, and ~> 5.0 quietly keeps you a major version behind. If you generate infrastructure code with either model, bump the constraint by hand and run tofu init -upgrade.

4. Agent task one: a broken nginx host

The first agent task was a server that should serve /var/www/shop on port 8080 and survive a reboot. We broke it three ways. The stock default site was moved to port 8080, so nginx refused to start with a duplicate default server. The site directory and file were root-only at 700 and 600, and nginx was stopped and disabled. A fourth fault was hidden on purpose: the shop config was missing the semicolon after index index.html, which nginx accepts silently because it reads server_name as two more index files.

Both models fixed all four faults in all three runs, including the silent semicolon. The grading script confirmed HTTP 200 with the page, a clean nginx -t, the service enabled and active, and nothing world-writable. Where they differed was style:

• Opus 5.5 (5 to 6 tool calls, $0.095 to $0.124, 36 to 47 s): moved the default site back to port 80 and set 755 and 644. In every run it also searched cron, tmpfiles and systemd overrides for anything that would undo the fix after a reboot, and it said plainly that it hadn’t rebooted.
• GPT-6.1 Sol (4 to 5 tool calls, $0.030 to $0.039, 26 to 32 s): granted access through the group, root:www-data with 750 and 640, which is tighter. In one run it unlinked the default site instead of moving it.

Sol’s permission fix is the one we’d rather see on a shared host, and Opus’s reboot audit is the one we’d rather see on a box someone else configured. Neither choice changed the grade. On a task this size you are paying Opus roughly three times as much for the extra caution, so decide whether your hosts need it.

5. Agent task two: four Kubernetes faults behind a default-deny policy

The second task was harder to read from the outside. A shop Deployment in namespace store had a typo in its image tag and mounted a ConfigMap that did not exist. Its Service had the wrong selector and the wrong target port, and the namespace was fenced by a default-deny ingress policy plus an allow rule for frontend only. The prompt told both models that only frontend should reach the app. Before either agent ran, the namespace looked like this:

Only the missing ConfigMap shows up in the events. The image typo stays hidden until the mount is fixed, and the Service problems never raise an event at all, so an agent that fixes the first error and declares victory fails. The grader then checked that a pod in frontend got the page, a pod in default was still blocked, both policies still existed and 2 of 2 replicas were ready.

The columns are model, turns, tool calls, cost in dollars, wall time in seconds and the grader’s verdict. All six runs passed, and neither model touched the network policies, which is the shortcut we were watching for. What separated them was one real k3s quirk. The embedded network policy controller needs a few seconds to add a brand-new pod’s IP to the allow list, so a throwaway kubectl run --rm curl pod in frontend gets “Connection refused” even when everything is fixed.

Opus 5.5 hit that race in all three runs. It tested with short-lived pods and got refused from frontend. Then it spent 4 to 9 commands on the detour, 2 to 7 of them reading iptables and ipset output, before it switched to long-running test pods, which is why one run took 18 tool calls and $0.77. If you have debugged k3s policies by hand, you have probably chased the same refusal. To its credit, all three final summaries blamed the short-lived test pods, run 3 named the few-second propagation delay precisely, and it never weakened the policy to make the error go away. GPT-6.1 Sol never saw the race: from the first check it started test pods with sleep, waited for them to be Ready and then curled. Its one stumble was a verification loop that ran into our 60-second tool timeout once per run, which it recovered from on the next turn.

Wall time came out close (107 to 150 s for Opus, 109 to 110 s for Sol) because both waited on rollouts. Cost did not: $1.60 for the three Opus runs against $0.19 for Sol. If you run agents like this through a CLI rather than a raw API, our AI coding agent test for DevOps work compares the tools around the models.

6. Speed: time to first token and output rate

Neither model is clearly faster. We streamed the Kubernetes prompt three times per model at default effort and timed the first visible token and the visible output rate after it:

Run	Opus 5.5 first token	Opus 5.5 visible tok/s	Opus 5.5 total	Sol first token	Sol visible tok/s	Sol total
1	11.3 s	217	15.2 s	9.5 s	230	11.4 s
2	13.3 s	224	18.0 s	19.0 s	164	21.8 s
3	11.2 s	187	16.9 s	13.7 s	260	15.4 s

If you stream answers to a user, Opus 5.5 was the steadier of the two, with first tokens between 11 and 13 seconds, while Sol ranged from 9.5 to 19 seconds on identical requests. Once text starts flowing both stream around 200 tokens a second. Sol usually finishes first for a simpler reason. It thinks for 240 to 407 tokens and writes about 450 visible tokens, where Opus thinks for 848 to 918 and writes about 1,000. The single-shot runs agree, with mean latencies of 7 to 16 seconds for Opus and 8 to 13 for Sol at default effort.

Where each model falls short

Neither model failed a task, so the weak spots are about cost, habits and the edges of each API:

• Opus 5.5 runs up the bill on long loops: its thoroughness turned one k3s timing quirk into 4 to 9 detour commands and an 8x cost gap. Budget agent runs by tool calls, not by list price.
• Opus 5.5 writes more than you asked for: the extra variables and traps are good engineering, but they are also more code for you to review, and they made its backup scripts two to three times longer than Sol’s.
• GPT-6.1 Sol can’t switch reasoning off: reasoning.effort starts at low, so there is no zero-thinking mode for cheap classification calls. Our GPT-6.1 Sol tests captured the HTTP 400 that none returns.
• GPT-6.1 Sol charges more for huge prompts: above 272K input tokens the whole request moves to $4 and $15, which narrows its advantage on whole-repo prompts.
• Both trail the third-party leaderboards in places that matter to you: Opus 5.5 lags Sol by 17 points on SRE Bench, and Sol lags Opus by 10 to 27 points on Vals AI’s Terminal-Bench 4.0 and SWE-bench Multimodal runs.

The stale AWS provider constraint showed up in both, so treat that as a model-era problem rather than a vendor one. Any generated Terraform needs its provider version checked against the registry before it goes near a real account.

Which one to pick

For most teams paying per token, the answer is GPT-6.1 Sol, and you can move to Opus 5.5 for the jobs where its strengths show up in your own evals. Match the model to the job:

• You run high-volume agents or CI bots: GPT-6.1 Sol. It passed the same tasks with fewer tool calls at 3 to 8 times lower cost per task.
• You need the best score on long, open-ended agent benchmarks: Opus 5.5. It leads Terminal-Bench 4.0, GDPval-AA and SWE-bench Multimodal on every third-party run we found.
• Your prompts regularly exceed 272K tokens: test both. Sol’s long-context surcharge brings its per-token input rate up to Opus’s, so the gap narrows to the output rate and Opus’s higher token count.
• You want code that defends itself without being asked: Opus 5.5, as long as you’re happy reviewing longer files.
• You already pay for Claude Code seats: stay with Opus 5.5 there, and consider Claude Sonnet 5.5 when you want an Anthropic model at Sol’s list price.

If you’re coming from the previous generation, the picture has shifted since our GPT-6 Astra vs Claude Opus 5 tests. This round used a new task set, so the numbers don’t line up one to one. The lesson carries over, though: when both models pass, cost per finished task is the number you should watch.