Claude Jupiter v1-p vs GPT-5.5 Benchmark: Real API Test on Reasoning and Coding

title: Claude Jupiter v1-p vs GPT-5.5 Benchmark: Real API Test on Reasoning and Coding slug: jupiter-vs-gpt55-benchmark-2026 summary: We tested claude-jupiter-v1-p and gpt-5.5 through https://cn.crazyrouter.com/v1 across reasoning, coding, patching, JSON, long-context recall, agent planning, and math tasks. GPT-5.5 scored slightly higher, while Jupiter was much faster but required a payload compatibility fix. tag: Benchmark language: en cover_image_url: https://raw.githubusercontent.com/xujfcn/images/main/blog/covers/jupiter-vs-gpt55-benchmark-2026.webp meta_title: Claude Jupiter v1-p vs GPT-5.5 Benchmark 2026 | Crazyrouter meta_description: Real API benchmark using https://cn.crazyrouter.com/v1 comparing Claude Jupiter v1-p and GPT-5.5 on reasoning, coding, structured output, long context, and agent planning. meta_keywords: claude jupiter v1-p, gpt-5.5, ai model benchmark, coding benchmark, crazyrouter api#

Claude Jupiter v1-p vs GPT-5.5: Real API Benchmark for Reasoning and Coding#

claude-jupiter-v1-p is an interesting model ID because it looks like a test or pre-release Claude route, while gpt-5.5 is the current high-end GPT route available through Crazyrouter.

Instead of guessing from the names, I ran both models through the same benchmark using the China endpoint:

Base URL: https://cn.crazyrouter.com/v1
Models tested:
- claude-jupiter-v1-p
- gpt-5.5
Date: 2026-05-27

The goal was not to create a massive academic benchmark. The goal was more practical:

If I were routing real developer tasks, which model looks smarter, which model codes better, which one is faster, and what hidden API compatibility issues would matter in production?

Short conclusion#

Here is the result from the final runnable test:

My reading:

• GPT-5.5 won narrowly on quality: 63.6/70 vs 61.8/70.
• Claude Jupiter v1-p was much faster: 5.17s average latency vs 10.44s.
• Both models completed all seven tasks in the fair run.
• Jupiter has an important compatibility caveat: with temperature: 0 included in the OpenAI-compatible payload, it returned 400 invalid_request on every task. Removing temperature made it pass 7/7.

So the practical conclusion is:

GPT-5.5 is the safer quality winner.
Claude Jupiter v1-p is surprisingly competitive and faster, but needs payload compatibility checks before production use.

The most important finding: payload compatibility matters#

The first run used the same OpenAI-compatible payload for both models:

{
  "model": "claude-jupiter-v1-p",
  "messages": [...],
  "temperature": 0,
  "max_tokens": 900
}

Result:

At first glance, that looks like Jupiter failed the benchmark.

But a compatibility probe showed the real issue: Jupiter currently rejects this payload shape when temperature: 0 is included.

I tested several payload variants:

This matters because production systems often assume OpenAI-compatible parameters are universally accepted. They are not.

For real routing, the correct health check is not just:

Is the model visible in /v1/models?

It should be:

Can the model handle my exact production payload?

Benchmark design#

I used seven tasks designed to reflect practical intelligence and developer usefulness:

Scoring was heuristic but answer-key based. The raw outputs and scoring JSON are saved with the benchmark so the result can be inspected.

Per-task results#

A few observations stand out.

1. Reasoning: both solved the logic puzzle#

Both models correctly solved the region/datastore puzzle:

A = Tokyo / Postgres
B = Singapore / S3
C = Frankfurt / Redis

Both scored 9/10. GPT-5.5 gave a more compact answer. Jupiter gave a longer explanation but reached the same result faster.

2. Coding: GPT-5.5 was slightly cleaner on the algorithm task#

The topKFrequent(words, k) task required:

• frequency descending;
• lexicographic tie-break;
• handling k <= 0 and empty input;
• better than O(n²).

GPT-5.5 explicitly used localeCompare for tie-breaking and got 9.6/10.

Jupiter also produced a correct implementation, using a direct comparison expression:

entries.sort((a, b) => b[1] - a[1] || (a[0] < b[0] ? -1 : a[0] > b[0] ? 1 : 0));

That is valid, but GPT-5.5's answer was slightly cleaner and easier to read.

3. Patch generation: both were excellent#

Both models fixed the Python retry function correctly:

• initial attempt plus retries retries;
• preserve and raise the final exception;
• no sleep after the final failed attempt;
• return a unified diff.

Both scored 10/10.

4. JSON extraction: both were perfect#

Both returned valid strict JSON with:

• service;
• severity;
• 27-minute duration;
• connection pool exhaustion root cause;
• customer_visible: true;
• mitigation actions.

Both scored 10/10.

5. Long-context recall: both passed#

The long-context test buried two important facts among repeated filler:

Jupiter can leave evaluation only after payload stability reaches 99%.
Optimize cost per successful task, not token price.

Both models recalled the key facts correctly.

6. Agent planning: both were strong#

Both models produced an 8-point safe execution policy for an AI coding agent, covering:

• permission boundaries;
• test gates;
• rollback;
• logging;
• model fallback;
• human escalation.

GPT-5.5 was marginally more concise. Jupiter was more detailed.

7. Math/cost reasoning: both got the important answer#

The math problem:

1,200,000 monthly requests
900 input tokens/request
250 output tokens/request
Model X: $0.80/M input, $2.40/M output
Model Y: 35% cheaper but 8% retries

Correct calculation:

Model X = $864 input + $720 output = $1,584.00
Model Y = ($1,584 × 0.65 × 1.08) = $1,111.97
Savings = $472.03/month

Both models produced the correct final conclusion: Model Y is cheaper by about $472.03/month.

What this means for developers#

If you are choosing a default model for coding and agent workflows, I would not make the decision based only on raw score.

I would separate three layers:

Layer 1: Quality#

GPT-5.5 is slightly ahead in this test. It was cleaner on algorithm implementation and more concise in several tasks.

Layer 2: Speed#

Jupiter was much faster in this sample:

Jupiter average latency: 5.17s
GPT-5.5 average latency: 10.44s

That is a big difference if you are building interactive coding tools or agent loops.

Layer 3: Payload stability#

This is where Jupiter needs caution.

The model worked well after removing temperature, but failed completely with temperature: 0 in the payload.

For production, that means you should not simply add it to your model list and route traffic blindly. You should run route-specific health checks:

1. Test /v1/models visibility.
2. Test your exact chat payload.
3. Test streaming if you use streaming.
4. Test tools/function calling if your agent uses tools.
5. Test structured JSON output.
6. Record 400/empty-output/timeout separately.

Recommended routing policy#

Based on this benchmark, I would route like this:

Reproducibility#

This benchmark used:

Base URL: https://cn.crazyrouter.com/v1
Endpoint: /chat/completions
Models: claude-jupiter-v1-p, gpt-5.5
Tasks: 7
Scoring: answer-key heuristic scoring plus manual inspection of key outputs

Important payload note:

GPT-5.5 used temperature=0.
Claude Jupiter v1-p omitted temperature because compatibility testing showed temperature=0 caused 400 invalid_request.

That is not a minor detail. It is one of the main findings.

Final verdict#

My conclusion:

GPT-5.5 is still the better default if you optimize for quality and production confidence.
Claude Jupiter v1-p is much more capable than a simple test placeholder and was faster in this run, but it must stay behind payload compatibility checks.

If Jupiter's parameter compatibility improves, it could become a very interesting low-latency coding and agent workflow candidate.

But today, I would not replace GPT-5.5 with Jupiter as a default production model.

I would add Jupiter to an evaluation lane, run it against real payloads, and promote it only when route-level stability is proven.