# Debugging Benchmark: DeepSeek V4 Pro vs MiMo V2.5 Pro

> Source: <https://dev.to/sl4m3/debugging-benchmark-deepseek-v4-pro-vs-mimo-v25-pro-29lm>
> Published: 2026-06-30 20:08:02+00:00

*A real-world comparison of two LLMs on a genuine race condition bug from GitHub*

| Metric | DeepSeek V4 Pro | MiMo V2.5 Pro |
|---|---|---|
| Time | ~8 min (2 rounds) | ~15 min (2 rounds) |
| Tokens | 2.43M | 3.36M |
| Cache hit rate | 92.1% | 95.2% |
| Cost | $0.14 (6% top-up fee) | $0.13 (0% fee) |
| Bugs found | 1 race condition | 3 race conditions |
| Fix approach | Prevention (lock-based) | Prevention (three-phase separation) |

**Verdict:** MiMo is better at debugging (finds more bugs, deeper analysis) AND cheaper. DeepSeek is faster and better for writing code.

Most LLM benchmarks test coding ability — write a function, solve a puzzle, implement an algorithm. But in real-world development, **debugging is harder than writing code**. You need to:

We wanted to test this specific skill. So we took a **real race condition bug** from a popular open-source library and gave it to both models.

**Repository:** [encode/httpcore](https://github.com/encode/httpcore)

**Issue:** [#961 - Race Condition After Async Cancellations Breaks Connection Pool](https://github.com/encode/httpcore/issues/961)

**Fix PR:** [#880 - Safe async cancellations](https://github.com/encode/httpcore/pull/880)

httpcore is a low-level HTTP client library used by httpx (the popular Python HTTP client). It handles connection pooling, HTTP/2, proxies, and more.

When async tasks are cancelled during connection operations, the pool's internal state becomes inconsistent. The pool thinks connections are still in use when they're actually cancelled, leading to **pool exhaustion** — new requests can never acquire a connection.

`connection_pool.py`

, `connection.py`

, and `http2.py`

We gave each model the **entire httpcore project** at the commit BEFORE the fix (commit `79fa6bf`

). The project included:

`README.md`

with bug description (no hints about the fix)`PROMPT.md`

with instructions`SOLUTION.md`

and `SOLUTION.diff`

(hidden from models)**Prompt (identical for both models):**

```
You are given a Python project with a bug. Your task is to find the bug
and write a detailed explanation of how to fix it.

1. Read README.md to understand the project and the bug description.

2. Analyze the source code in httpcore/_async/ and httpcore/_sync/
   to find the root cause of the race condition.

3. Run the tests to see which ones fail:
   pip install -e ".[asyncio]"
   pytest tests/ -v

4. Write your findings to SOLUTION.md with:
   - Root cause analysis (what exactly goes wrong)
   - Why it happens (the mechanism)
   - How to fix it (the approach, not necessarily the exact code)
   - Which files need to be changed

Do NOT modify the source code. Only write SOLUTION.md.
```

After Round 1, both models proposed **patches** (cleanup handlers) rather than **prevention** (atomic state management). We gave them a hint:

**Prompt (identical for both models):**

```
Your previous fix handled orphaned connections in the cancellation
handler. This works, but it treats the symptom — connections still
get orphaned, you just clean them up after.

A better approach would be to prevent the race condition from
happening in the first place. The root cause is that state
management (tracking idle vs in-use connections) is interleaved
with I/O operations (queue.get(), queue.put()). When a task is
cancelled between state update and I/O, the pool loses track.

Can you find a way to make the state management atomic — so that
cancellation cannot happen midway through the acquire/release
sequence?

Write your refined solution to SOLUTION_V2.md.
```

**Root Cause:** Found 1 race condition — orphaned connections when task is cancelled after assignment but before resume.

**Key Insight:** "The connection remains in the pool marked as 'in use' but the task that was supposed to use it is gone."

**Proposed Fix:** Handle orphaned connections in the cancellation handler — check if a connection was assigned and release it.

**Quality:** Excellent root cause analysis, step-by-step mechanism explanation. However, proposed fix was a patch (cleanup handler), not prevention.

**Root Cause:** Found **3 distinct race conditions**:

**Key Insight:** Explained why existing tests don't catch it — they use single-request scenarios.

**Proposed Fix:** Add cleanup handlers + defensive connection sweep.

**Quality:** Excellent analysis, deeper than DeepSeek (3 bugs vs 1). However, proposed fix was also a patch (cleanup handlers), not prevention.

| Aspect | DeepSeek | MiMo |
|---|---|---|
| Time | ~3 min | ~9 min |
| Bugs found | 1 | 3 |
| Fix approach | Patch (cleanup) | Patch (cleanup) |
| Fix quality | 🟡 Treats symptoms | 🟡 Treats symptoms |
| Explanation quality | Excellent | Excellent |

**Approach:** Move connection claiming to the waiting task, make it atomic inside a lock.

**Key Changes:**

`_wait_and_acquire()`

method`response_closed()`

`_pool_state_changed`

event**Quality:** 🟢 Architecturally clean, similar to the actual fix.

**Approach:** Three-phase separation — CLEANUP (I/O), STATE (sync), I/O (network).

**Key Changes:**

`_attempt_to_acquire_connection`

is now synchronous (no await inside lock)`AsyncShieldCancellation`

for critical sections**Quality:** 🟢 Systematic approach, analyzed 5 cancellation scenarios.

| Aspect | DeepSeek | MiMo |
|---|---|---|
| Time | ~5 min | ~6 min |
| Approach | Lock-based atomic | Three-phase separation |
| Complexity | Medium | High |
| Edge cases | Good | Excellent (5 scenarios) |

| Metric | DeepSeek V4 Pro | MiMo V2.5 Pro |
|---|---|---|
| Total tokens | 2,431,121 | 3,356,951 |
| Cache hit | 2,198,400 | 3,146,304 |
| Cache miss | 189,058 | 157,502 |
| Output | 43,663 | 53,145 |
| Cache hit rate | 92.1% | 95.2% |
| API requests | 30 | 34 |

Both models use the same pricing on OpenCode Go:

**DeepSeek V4 Pro:**

**MiMo V2.5 Pro:**

Even though MiMo used **38% more tokens**, it was still cheaper because:

The actual fix by Tom Christie (httpcore author) was elegantly simple:

**Approach:** Move ALL state management into non-cancellable sections using locks.

**Key Insight:** "The async case cannot have cancellations or context-switches midway through the state management because we hold the lock."

**Files Changed:** 9 files, +512/-379 lines

Both models converged on this approach in Round 2, though with different implementations:

| Task | Better Model | Why |
|---|---|---|
Writing code |
DeepSeek V4 Pro | Faster, fewer tokens, cleaner architecture |
Debugging |
MiMo V2.5 Pro | Finds more bugs, deeper analysis, cheaper |

For this specific debugging task:

**MiMo is both better at debugging AND cheaper.** The higher token usage is offset by the lack of top-up commission.

Synthetic bugs are too easy — models solve them in seconds. Real bugs from production codebases require:

In real-world debugging, you often get a quick fix first, then refine it. Testing both rounds shows:

This benchmark reveals that **debugging and code writing are different skills**. DeepSeek excels at writing clean, efficient code quickly. MiMo excels at deep analysis and finding subtle bugs.

For teams building AI-assisted development tools:

The surprise finding: **MiMo is cheaper for debugging despite using more tokens**, thanks to zero commission on top-up. For high-volume debugging workloads, this cost difference adds up.

*Benchmark conducted on June 30, 2026 using DeepSeek API and Xiaomi MiMo API platforms. Full benchmark data available in the author's GitHub repository.*