Surge AI

Inkling 2 Small redefines sudo L7's cost-performance frontier, our benchmark for staff-level software engineering.

It's the cheapest model on sudo L7 ($0.34 per task!), while punching way above - coming within two points of Gemini 4 Argon and Muse Wonder 1.0 at a fraction of the cost.

➡️ Gemini 4 Argon: 2 points higher at 7.8x the cost ($2.64)
➡️ Muse Wonder 1.0: 2 points higher at 13x the cost ($4.39)
💰 Inkling 2 Small: $0.34

And it outscores models that cost 3–7× more:

➡️ Muse Spark 1.3: 5.4x the cost ($1.85)
➡️ Gemini 3.8 Flash: 7.3x the cost ($2.47)
➡️ GLM 5.3: 3.4x the cost ($1.15)

Congrats to the @thinkymachines team!

How far can great RL data push an open model? Far enough to beat frontier closed models.

We trained @NVIDIAAI's Nemotron 3.5 Super VL (just 12B active parameters) on Surge RL data using @thinkymachines' Tinker.

The result? It beat GPT-5.6 Terra's published score on SWE-Bench Pro V2, and also improved across 11 other benchmarks:

➡️ GDP.pdf: +18.6pp
➡️ HANDBOOK.md: +12.8pp
➡️ AutomationBench: +12.7pp
➡️ Chartography: +11.2pp
➡️ AdvancedIF: +7.7pp

There's enormous untapped potential in open models. We believe that frontier performance doesn't have to come from frontier size. It can come from better data and better post-training, and we're only scratching the surface.

Remember the era of fancy prompt engineering? Do elaborate coding agent harnesses actually make agents better?

We benchmarked popular coding setups on 100 real-world software engineering tasks using sudo L7.

The results:
➡️ Best harness additions: +2–3 points
➡️ More thinking: +11.3 points!
➡️ One coding agent setup: 8× the cost, no improvement

In one case, a harness spawned 17 agents to review a simple email change, and all 17 still missed a critical issue.

Sometimes smarter thinking beats more agents.

We’ve spent years helping frontier labs find where their models fail and building the data to make them better.

Now, as part of the @FireworksAI_HQ Frontier Ecosystem, we’re bringing that expertise to enterprises building on Fireworks.

Build with both: surgehq.ai/fireworks

The best model depends on the job.

A model that excels at coding may struggle with financial analysis. Models with similar headline scores can behave very differently when the assignment requires judgment, verification, or navigating dozens of messy files.

That’s why we built DAYJOB and sudo L7 around work that professionals actually do.

Today, six of those benchmarks joined @FireworksAI_HQ Specialized Intelligence Index, giving teams another way to compare models against the work they need done.

Read more about what we test and how the benchmarks are built:
surgehq.ai/blog/fireworks…