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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:

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:

Model	Success rate	Total score	Average score	Average latency	Median latency	Total tokens
claude-jupiter-v1-p	7/7	61.8/70	8.83/10	5.17s	3.35s	6096
gpt-5.5	7/7	63.6/70	9.09/10	10.44s	9.63s	3802

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:

The most important finding: payload compatibility matters#

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

Result:

Model	Tasks	Success	Result
claude-jupiter-v1-p	7	0/7	all returned 400 invalid_request
gpt-5.5	7	7/7	all completed

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:

Jupiter payload variant	Result
system + max_tokens + temperature=0	0/7
system + max_tokens, no temperature	7/7
no system, max_tokens, no temperature	7/7
messages only	7/7
short minimal prompt	1/1

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:

It should be:

Benchmark design#

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

Task	What it tests
logic_grid	constraint reasoning and contradiction handling
algorithm_design	coding ability, sorting, edge cases
bug_fix_patch	patch generation and exception correctness
json_schema_extraction	structured output reliability
long_context_recall	recall from a long prompt with distractors
agent_tool_plan	agent safety policy and workflow design
math_word_problem	arithmetic, cost modeling, retry reasoning

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#

Task	Jupiter score	GPT-5.5 score	Jupiter latency	GPT-5.5 latency
logic_grid	9.0/10	9.0/10	5.691s	11.287s
algorithm_design	8.0/10	9.6/10	2.411s	7.045s
bug_fix_patch	10/10	10/10	3.349s	9.628s
json_schema_extraction	10/10	10/10	2.118s	6.193s
long_context_recall	10/10	10/10	2.53s	2.335s
agent_tool_plan	9.8/10	10/10	13.838s	14.071s
math_word_problem	5/10	5/10	6.266s	22.511s

A few observations stand out.

1. Reasoning: both solved the logic puzzle#

Both models correctly solved the region/datastore puzzle:

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:

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.

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:

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:

Correct calculation:

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:

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:

Recommended routing policy#

Based on this benchmark, I would route like this:

Use case	Recommended model
highest-quality reasoning/coding default	GPT-5.5
latency-sensitive coding helper after compatibility validation	Claude Jupiter v1-p
JSON extraction / simple structured tasks	either model
agent planning and safety policy	either model, GPT-5.5 slightly safer
production routing without custom health checks	GPT-5.5
experimental model lane	Claude Jupiter v1-p

Reproducibility#

This benchmark used:

Important payload note:

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

Final verdict#

My conclusion:

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.