DeepSeek V4 vs Claude Opus 4.5: Can It Beat 80.9% on SWE-bench?

Hey fellow AI tool testers — if you're the kind of person who runs models against real repos instead of believing the hype, this one's for you.

I've spent the last three years stress-testing coding models in actual workflows. Not demos. Real pull requests, refactors that span 15+ files, bug fixes that need context from legacy code written five years ago. The kind of work where a model either holds up or falls apart.

Right now, there's noise around DeepSeek V4. Internal sources claim it'll beat Claude Opus 4.5's 80.9% SWE-bench Verified score—the current industry record for AI coding. Launch is supposedly mid-February 2026, possibly February 17 to align with Lunar New Year.

Here's my question: Can V4 actually survive repository-scale work, or is this another benchmark headline that doesn't translate to daily coding?

I'm not here to crown a winner before V4 even drops. What I did instead: I went back through my test logs from the past month running Claude Opus 4.5 on the same messy codebases I'll throw at V4 when it launches. This is what I'm watching for—and what you should too if you're deciding whether to switch models or stick with what's working.

Current Coding Benchmark Leaders

Claude's SWE-bench Dominance

Claude Opus 4.5 hit 80.9% on SWE-bench Verified in November 2025. That's real GitHub issues from production repos—not toy problems. It's the first model to break 80%, outperforming GPT-5.1 (76.3%) and Gemini 3 Pro (76.2%).

I've been running Opus 4.5 since launch. Here's what that number actually means in practice:

What works: Multi-file refactors where the model needs to trace dependencies across 20+ files. I gave it a legacy Django project with circular imports and asked it to untangle the mess. It handled it. Not perfectly—I had to step in twice—but it maintained coherent understanding across the entire codebase.

Where it hesitates: Ambiguous requirements. If I write a vague prompt like "make this faster," Opus asks clarifying questions instead of guessing. That's frustrating when I want instant output, but it prevents the kind of hallucinated refactors that create more problems than they solve.

Here's the kicker: Opus 4.5 also leads on Terminal-Bench at 59.3%, ahead of Gemini 3 Pro (54.2%) and GPT-5.1 (47.6%). Command-line proficiency matters when you're automating deployment scripts or debugging production environments.

Data source: Anthropic official benchmarks, Vellum analysis

DeepSeek's Internal Claims

DeepSeek reportedly aims to beat 80.9% with V4. Sources with direct project knowledge told The Information that internal benchmarks show V4 outperforming both Claude and GPT series—especially on extremely long code prompts.

Key architectural changes backing these claims:

1. Manifold-Constrained Hyper-Connections (mHC): Published January 1, 2026 in a research paper co-authored by DeepSeek founder Liang Wenfeng. This addresses training instability in trillion-parameter models.
2. Engram Conditional Memory: Published January 13, 2026. Tests on a 27B parameter model showed a jump from 84.2% to 97% on Needle-in-a-Haystack tests—directly relevant to long-context coding.
3. 1 million+ token context window: V4 can allegedly process entire medium-sized codebases in a single pass, understanding import relationships across dozens of files.

Here's where I pause: Internal benchmarks aren't independent verification. DeepSeek's V3 matched GPT-4 performance at a fraction of the training cost ($6 million vs. $100 million), so efficiency innovation is real. But "outperforms in internal tests" doesn't mean it'll handle my Django project better than Opus 4.5 did.

What I'm waiting for when V4 drops:

• Public SWE-bench score: Does it actually beat 80.9% or fall at 78-79%?
• Long-context stability: Can it handle million-token coding tasks without hallucinating?
• Real repo behavior: Does it maintain coherence when I throw it a 50-file refactor?

Where Each Model Wins

Long Context (V4's Advantage)

DeepSeek V4's million-token context window combined with Engram memory could change repository-scale work. Current models—including Opus 4.5—struggle when context exceeds their training distribution. They either truncate important details or slow down significantly.

The real test: I have a monolithic Node.js app (87 files, ~45,000 lines) with dependency chains that span the entire codebase. When I ask Opus 4.5 to refactor a core utility function, it handles 15-20 files before context management becomes visible. It doesn't fail—it just starts asking me to confirm relationships it should infer.

If V4's Engram memory works as advertised, that friction disappears. The model recalls specific details from massive documents without computational penalty. That's the difference between "useful assistant" and "credible pair programmer."

Code example - What I'll test with V4:

# Scenario: Legacy Python codebase with circular imports
# File 1: user_service.py imports from permissions.py
# File 2: permissions.py imports from user_service.py
# 30+ other files depend on both

# Task: Refactor to break circular dependency
# Success = model traces all dependencies and proposes clean architecture
# Failure = model suggests changes that break 10 other modules

I'll run this exact scenario on V4 launch day. If it handles it without breaking anything, that's a workflow shift. If it hallucinates imports or misses side effects, it's just hype.

Reliability (Claude's Strength)

Opus 4.5 doesn't hallucinate libraries. When I ask it to use a specific package version, it checks compatibility. When I request a minimal patch, it doesn't "improve" things I didn't ask about.

Real example from my test logs (January 28, 2026):

Prompt: "Fix the timezone test without changing production code. Explain the root cause in one sentence."

Opus 4.5 response:

# Root cause: Test uses naive datetime, production expects aware datetime

@pytest.fixture
def aware_now():
    return datetime.now(timezone.utc)

def test_scheduled_task(aware_now):
    # Changed assertion to use aware datetime
    assert task.scheduled_at >= aware_now

That's surgical. No refactoring I didn't ask for. No "while we're here, let's also..." suggestions that create scope creep.

Where this matters: When you're fixing a critical bug in production code at 2 AM, you don't want the model to rewrite your error handling strategy. You want the minimum viable fix.

I'll test V4 with the same prompt. If it returns a three-file refactor or suggests architectural changes, that's a red flag. If it matches Opus 4.5's restraint, then we're comparing apples to apples.

What to Watch at Launch

When DeepSeek V4 drops (likely February 17, 2026), here's my evaluation framework—the same one I used for Opus 4.5:

1. SWE-bench Verified Score (Public)

• Does it beat 80.9%?
• By how much? (2% is marketing noise, 5%+ is meaningful)
• Which types of issues does it solve that Claude misses?

1. Long-Context Stress Test

• Can it handle my 87-file Node.js app?
• Does context retrieval stay accurate past 500k tokens?
• Latency: How long does it take to process the full repo?

1. Real Bug Fixing I'll run both models on 10 identical bug reports from my backlog:

• Stack traces spanning multiple files
• Legacy code with unclear ownership
• Bugs that require understanding business logic, not just syntax

Success metric: Which model produces fixes I can commit without modification?

1. Tool Integration

• Can I wire it into my VSCode workflow without friction?
• Does it play well with git, linting, and test runners?
• Pricing: What's the actual cost per pull request at scale?

1. Failure Behavior

• What happens when it doesn't know?
• Does it hallucinate with confidence or admit uncertainty?
• Can I trust it to say "I need more context" instead of guessing?

I'll publish these results on my blog the week after launch. No cherry-picked demos—just the same messy repos I throw at every model.

The Real Question

This isn't about which model wins on a leaderboard. It's about which one survives your actual workflow.

When V4 launches, I'll run the same test suite I ran on Opus 4.5, GPT-5.1, and every other "coding breakthrough" over the past year. Most models look impressive in demos and fall apart when you ask them to refactor a six-year-old Express app with no documentation.

The ones that matter are the ones that make it into my daily workflow and stay there. That's the only benchmark that counts.

If you’re the kind of person who tries new AI tools but drops them once they become complicated or fragmented, that friction is usually the deal-breaker.We built Macaron for people who want to test real tasks in one place—without juggling multiple apps, setups, or mental context—so you can decide calmly whether something is worth keeping.If you’re curious, try it with one real task you already have and see if the experience actually fits how you work.

FAQ

Q: Should I wait for V4 or stick with Claude Opus 4.5?

If you're already using Opus 4.5 in production workflows, there's no reason to pause. It works. The 80.9% SWE-bench score isn't hype—I've validated it in real repos.

When V4 launches, run your own tests. Take your most annoying refactor, the one where you currently need to intervene three times, and see if V4 reduces that to zero. If it does, switch. If not, stay put.

Q: Will V4 be open-source?

Likely. DeepSeek released V3 and R1 with open weights. If V4 follows that pattern, you can run it locally on consumer hardware (dual RTX 5090s for the quantized version). That's a different value proposition than API-only access.

For enterprises with strict data governance—finance, healthcare, defense—local deployment eliminates the "sending proprietary code to external APIs" concern.

Q: What about cost?

Claude Opus 4.5: $5 per million input tokens, $25 per million output tokens.

DeepSeek V3 pricing (for reference): $0.27 per million input tokens.

If V4 maintains that efficiency, the cost gap is massive. But efficiency doesn't matter if the model produces code I can't use. I'll track real cost-per-working-solution, not just token pricing.

Q: Which one is better for junior developers?

Neither. If you're learning to code, both models will teach you to rely on AI instead of understanding fundamentals. Use them after you can write the code yourself—then they become force multipliers.

For senior developers who already know how to debug, refactor, and architect systems? Both models are useful. The question is which one reduces friction in your specific workflow.

Q: Will DeepSeek V4 replace GitHub Copilot?

GitHub Copilot has 42% market share and runs in 90% of Fortune 100 companies. That's not just about benchmark scores—it's about ecosystem integration, enterprise SLAs, and regulatory compliance.

V4 needs more than coding performance to crack that dominance. It needs VSCode extensions, JetBrains plugins, team management features, and compliance certifications. Benchmark wins are necessary but not sufficient.