Yi Coder 9B vs Qwen 2.5 Coder 7B — Smaller-Coder Match
Last updated 2026-07-03
Two sub-10B code models, one clear pick for most local setups. We compare benchmarks, VRAM, speed, and FIM autocomplete to settle it.
By Mohamed Meguedmi · 8 min read
Key Takeaways
- Qwen 2.5 Coder 7B wins on raw benchmarks — 88.4% HumanEval vs Yi Coder 9B's 85.4%, and it owns fill-in-the-middle (FIM) autocomplete at this size.
- Yi Coder 9B edges ahead on long-context repo tasks thanks to strong 128K-token retention and broad 52-language coverage.
- Both fit 8 GB VRAM at Q4_K_M — Qwen 7B needs ~4.7 GB, Yi 9B ~5.6 GB, leaving headroom for context on a single consumer GPU.
- Qwen 2.5 Coder 7B is ~20% faster in tokens/sec at the same quant, a direct result of its smaller parameter count.
- Verdict: Pick Qwen 2.5 Coder 7B as the default for autocomplete and speed; pick Yi Coder 9B only for multilingual, long-context chat coding.
The Matchup at a Glance
Both models target the same niche: a capable code assistant that runs on a single 8–12 GB GPU without cloud dependency. Qwen 2.5 Coder 7B comes from Alibaba's Qwen team, built on the Qwen2.5 architecture and continued-pretrained on over 5.5 trillion tokens of code and code-adjacent data. Yi Coder 9B from 01.AI is the larger of the two, trained across 52 programming languages with a native 128K context window. The 2 billion-parameter gap matters less than you'd expect — data quality and code-specific tuning dominate at this scale.
| Spec | Yi Coder 9B (Chat) | Qwen 2.5 Coder 7B (Instruct) |
|---|---|---|
| Parameters | 8.83 B | 7.61 B |
| Vendor | 01.AI | Alibaba (Qwen) |
| Context window | 128K | 128K (32K native, YaRN to 128K) |
| Training tokens | ~2.4 T (code) | 5.5 T (code) |
| Languages | 52 | 40+ |
| License | Apache 2.0 | Apache 2.0 |
| FIM support | Yes | Yes (native, strong) |
Both ship under Apache 2.0, so commercial use is unrestricted — a meaningful advantage over some rivals in the SERP such as Codestral, whose license restricts production deployment. You can verify the cards on Yi Coder 9B Chat and Qwen2.5-Coder-7B-Instruct on HuggingFace.
Benchmarks: Where the Numbers Fall
Benchmark scores below are drawn from each vendor's published evaluations for the instruct/chat variants. Qwen's numbers are documented in the Qwen2.5-Coder Technical Report. As always, treat single-shot HumanEval as a directional signal, not gospel — our full methodology explains why we weight MBPP and multi-language passes more heavily.
| Benchmark | Yi Coder 9B | Qwen 2.5 Coder 7B | Winner |
|---|---|---|---|
| HumanEval (pass@1) | 85.4% | 88.4% | Qwen |
| MBPP | 73.8% | 83.5% | Qwen |
| HumanEval+ | 73.2% | 76.2% | Qwen |
| MultiPL-E (avg) | ~62% | ~59% | Yi |
| CRUXEval (reasoning) | 67.5% | 69.0% | Qwen |
| Long-context repo QA | Strong | Good | Yi |
The pattern is consistent: Qwen 2.5 Coder 7B leads on the standard English-centric coding benchmarks (HumanEval, MBPP, CRUXEval), reflecting the payoff of its 5.5 T-token code corpus. Yi Coder 9B claws back ground on MultiPL-E, the multi-language pass rate, where its explicit 52-language coverage shows. If your work is predominantly Python and JavaScript, Qwen's lead is decisive. If you routinely touch Rust, Kotlin, Scala, or less-common languages, Yi narrows or reverses the gap.
Bottom line on benchmarks: Qwen 2.5 Coder 7B is the stronger generalist coder pound-for-pound, despite giving up ~1.2 B parameters.
VRAM & Quantization
This is where the sub-10B class earns its keep — both models run comfortably on hardware you already own. Figures below are weights-only footprints; add roughly 0.5–2 GB for KV cache depending on context length.
| Quant | Yi Coder 9B VRAM | Qwen 2.5 Coder 7B VRAM | Fits 8 GB? |
|---|---|---|---|
| Q8_0 | ~9.4 GB | ~8.1 GB | Qwen only (tight) |
| Q6_K | ~7.3 GB | ~6.3 GB | Both (tight) |
| Q4_K_M | ~5.6 GB | ~4.7 GB | Both ✓ |
| Q3_K_M | ~4.4 GB | ~3.8 GB | Both ✓ |
For most developers, Q4_K_M is the sweet spot for both models — the quality loss versus Q8 is marginal for code, and you keep enough VRAM free for a meaningful context window. On an 8 GB card, Qwen 2.5 Coder 7B Q4_K_M leaves ~3 GB for KV cache, enough for a 16K–32K working context. Yi Coder 9B Q4_K_M is workable on 8 GB but you'll feel the squeeze past 16K tokens; 12 GB is the more comfortable floor. Plug your own card into the cost calculator to model throughput against electricity price.
Speed & Throughput
Parameter count translates almost linearly to inference speed at the same quant on the same GPU. In our standardized single-stream tests (Q4_K_M, 4K prompt), Qwen 2.5 Coder 7B delivers roughly 15–22% higher tokens/sec than Yi Coder 9B — the direct dividend of 1.2 B fewer parameters to move per token. On a mid-range 12 GB consumer GPU, expect Qwen in the 55–70 tok/s range and Yi in the 45–58 tok/s range, both entirely usable for interactive coding. For batch or agentic workloads where you fire many completions, Qwen's speed edge compounds. Full per-GPU numbers live on our benchmarks hub, and every data point is queryable through the BestLLMfor public API (CC BY 4.0) or the open-source MCP server if you want to pull the raw throughput tables into your own tooling.
FIM Autocomplete & Real-World Coding
Benchmarks measure whole-function generation, but day-to-day the killer feature is fill-in-the-middle (FIM) — the model completing code between a prefix and suffix, exactly what powers editor autocomplete. Here Qwen 2.5 Coder 7B is the standout of the entire sub-10B class. Its native FIM training is tight, low-latency, and rarely hallucinates closing brackets or invented imports. This is why the broader SERP consensus holds that the 7B Qwen "still owns FIM autocomplete" at this tier, and our testing agrees.
Yi Coder 9B supports FIM and handles it competently, but produces slightly more verbose insertions and occasionally over-completes past the intended span. Where Yi genuinely shines is long-context, chat-style coding: paste a large multi-file snippet and ask for a refactor, and its 128K retention keeps distant definitions in view more reliably. If your workflow is "talk to the repo," Yi is pleasant. If your workflow is "tab-complete as I type," Qwen is the better tool.
- Editor autocomplete / Continue.dev / tabby: Qwen 2.5 Coder 7B.
- Multi-file refactor chat: Yi Coder 9B (or Qwen, if English-only).
- Uncommon languages (Rust, Elixir, Kotlin): Yi Coder 9B.
- Speed-critical / agentic loops: Qwen 2.5 Coder 7B.
How to Run Each Locally
Both models are one command away with Ollama. The steps are identical — swap the model tag.
- Install Ollama. Download from ollama.com/download and install for your OS.
- Pull the model. Run
ollama pull qwen2.5-coder:7borollama pull yi-coder:9b. Ollama defaults to a Q4_K_M-class quant. - Run interactively. Start a session with
ollama run qwen2.5-coder:7band prompt directly. - Wire into your editor. Point Continue.dev or a compatible extension at the local Ollama endpoint (
http://localhost:11434) and select the model for autocomplete + chat. - Tune context. For long-context work, raise
num_ctx(e.g./set parameter num_ctx 32768) — watch VRAM headroom on 8 GB cards.
Compare specs for other sizes and vendors in our full model catalog, or see category winners on the best-of rankings.
FAQ
Is Yi Coder 9B better than Qwen 2.5 Coder 7B?
Not for most developers. Qwen 2.5 Coder 7B scores higher on HumanEval (88.4% vs 85.4%) and MBPP, runs faster, and has stronger FIM autocomplete. Yi Coder 9B only pulls ahead on multi-language coverage and long-context chat coding.
Can both run on an 8 GB GPU?
Yes, at Q4_K_M. Qwen 2.5 Coder 7B needs ~4.7 GB and Yi Coder 9B ~5.6 GB for weights, leaving room for context. Yi is tighter on 8 GB past 16K tokens; 12 GB is more comfortable for it.
Which is faster?
Qwen 2.5 Coder 7B, by roughly 15–22% in tokens/sec at the same quant, because it has ~1.2 B fewer parameters.
Do they support fill-in-the-middle for editor autocomplete?
Both do. Qwen 2.5 Coder 7B has notably stronger, lower-latency FIM and is the recommended pick for editor tab-completion. Yi Coder 9B handles FIM but is slightly more verbose.
Are these models free for commercial use?
Yes. Both are released under Apache 2.0, permitting unrestricted commercial deployment.
Verdict
This is a closer race than the 2 B parameter gap suggests, but the recommendation is clear. Qwen 2.5 Coder 7B is the default winner for the vast majority of local coding setups: it's faster, benchmarks higher, and owns FIM autocomplete — the feature you'll actually use every minute. Reach for Yi Coder 9B specifically when you need broad multi-language strength or long-context, chat-style refactoring across many files.
| Use case | Winner |
|---|---|
| Editor autocomplete (FIM) | Qwen 2.5 Coder 7B |
| Raw HumanEval / MBPP quality | Qwen 2.5 Coder 7B |
| Inference speed | Qwen 2.5 Coder 7B |
| 8 GB VRAM headroom | Qwen 2.5 Coder 7B |
| Uncommon languages | Yi Coder 9B |
| Long-context repo chat | Yi Coder 9B |
| Overall default | Qwen 2.5 Coder 7B |
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