TLDR; DeepSeek V4 Pro 0813 tops our SWE-Bench and LiveCodeBench runs, costs a third of Fable 5 per solved task, and turns out to be a better routing partner than Kimi K3. Training is live.
Last week we enabled Deepseek on Fireworks with serverless endpoints, dedicated deployments, and training support. The model provides a 1M-token context window with native tool calling and preserved reasoning history, targeting long-horizon agentic workloads.
We ran DeepSeek V4 Pro 0813 through the same eval suite we used for Kimi K3 in July. We evaluated Deepseek against Kimi K3 and Fable 5 across four benchmark families: SWE-Bench Verified, LiveCodeBench v6, Aider Polyglot v1, and Terminal-Bench 2.1. Deepseek V4 Pro is the strongest single model in this combined eval on two of four families.
| Benchmark | Kimi K3 | Deepseek V4 Pro 0813 | Fable 5 |
|---|---|---|---|
| SWE-Bench Verified (500) | 92.6% | 95.2% | 85.4% |
| LiveCodeBench v6 (100) | 88.0% | 92.0% | 91.0% |
| Aider Polyglot (225) | 76.4% | 73.8% | 88.0% |
| Terminal-Bench 2.1 (89) | 80.9% | 76.4% | 76.4% |
Deepseek V4 Pro delivers a massive cost advantage to Fable 5 on standard code tasks, running $0.309 per solved task on SWE-bench (vs. $0.808 for Fable 5) and $0.040 on LiveCodeBench (vs. $0.225).
On Terminal-Bench there are two key things to note about Fable 5’s performance and costs:
Where Deepseek V4 Pro is not as strong is the multi-language breadth. As we demonstrated in our K3 analysis vs. Fable 5, model specialization varies widely by ecosystem. On Aider Polyglot, V4 Pro stays strong in Rust (90.0%) and C++ (80.8%), but falls to 48.9% on Java (the largest slice at 47 tasks) compared to Fable 5’s 74.5%.
Java performance accounts for nearly the entire gap. If you run a Java-heavy codebase, you may face bottlenecks if you use a single model. As we discovered with our K3 testing, multiple models is a better solution than using a single-model architecture. Routing tasks dynamically to specialist models yields better overall accuracy at a fraction of the cost.
We tested oracle routing between DeepSeek V4 Pro and Fable 5, picking the better model per task and breaking ties toward the lower cost one. As a quick refresher: oracle routing is a method for measuring the best theoretical performance by running the task through each model and then picking the cost-optimized correct option to establish the cost and performance ceiling.
On the 409-task combined cohort, routing hits 92.4% against Fable 5 alone at 86.6%. That is 5.9 points better than the frontier model, at $0.279 per task against Fable's $4.510. Sixteen times lower costs, and better. And 86.3% of tasks route to V4 Pro, which is the real finding: the expensive model is the exception, not the default.
Every V4 Pro pairing beats the equivalent K3 pairing. LiveCodeBench hits 94.0% against 92.0%, Aider reaches 93.3% against 91.6%, and the combined cohort lands at 92.5% against 91.1%. V4 Pro is a better router partner than K3 even though K3 scores higher on CyberGym, because V4 Pro's wins land on tasks Fable 5 misses.
It is worth noting what oracle routing is: an upper bound computed after the fact, not a policy you can deploy today. Real routers capture a fraction of it. This is important because the gap size tells you how much headroom exists.
Want to build a specialized model tailored to your specific domain? DeepSeek V4 Pro training is live on Fireworks with full support for SFT, DPO, and RFT. You get frontier-grade infrastructure to push model performance without running into artificial platform limits. Once your run finishes, you can deploy your fine-tuned checkpoint to production in a single click. Because training and serving run on the same co-optimized backend, your production performance matches your training benchmarks every time.
Deepseek’s own published benchmark runs use the DeepSeek Harness (dsh) which is a native agent runtime handling everything from the model adapter down to the core execution loop. It speaks Deepseek V4’s native tool semantics instead of routing calls through a generic OpenAI-style interface. For this model, Deepseek also has not ship a standard chat template, so small serialization mismatches can silently drop model performance.
During testing on Fireworks, we found that a "minimal-first" setup yields much more consistent tool calls than jumping straight to the full standard catalog. Starting with a bare bash and editor surface before promoting to dsh standard brings DeepSeek V4 Pro much closer to the setup DeepSeek used in their own benchmark runs.
Whether you are looking to cut unit costs on autonomous agents, run zero-refusal security audits, or build a specialized domain model, DeepSeek V4 Pro gives you frontier-grade capabilities at half the cost per solved task. Across 840 traced adversarial security runs, it recorded zero refusals and zero output-length truncations, guaranteeing your agents complete their workloads without hitting unexpected guardrails.
Here is how you can start building today:
Deploy Deepseek v4 Pro 0813 on Fireworks
