Does open source model really save you money on ai coding ?

When DeepSeek V4 and Kimi K3 landed with token prices a fraction of what the frontier labs charge, the reaction was predictable. If an open-weights model costs 100x less per token, surely it saves you money on AI coding? The honest answer is: usually yes, but not for the reason the sticker price suggests, and not always by as much as you'd think. Here's what the current numbers actually say.

The sticker prices are real, and they are dramatic

Pulling current figures from Artificial Analysis, the independent benchmarking site, the per-million-token prices span two orders of magnitude. DeepSeek V4 Flash runs about $0.14 in and $0.28 out. Kimi K3 sits at roughly $3 in and $15 out. Meanwhile GPT-5.6 Sol is around $5 in and $30 out, Claude Opus 5 is $5 in and $25 out, and Claude Fable 5 tops the list at $10 in and $50 out. On output tokens alone, DeepSeek is somewhere between 90x and 180x cheaper than the premium tier. That part of the hype is simply true.

Why the sticker price lies to you

Modern top-tier models are reasoning models. Before they answer, they emit a large hidden stream of thinking tokens, and two models solving the identical coding task can burn wildly different amounts of them. That means dollars-per-token is the wrong unit. The unit you actually pay in is dollars-per-completed-task.

Think of it like hiring a taxi and comparing drivers only by their per-mile rate. One takes the direct route; the other circles the block twenty times because he is thinking about the best way there. The cheap per-mile fare loses badly if the meter runs twenty times longer. What lands in your wallet is the total fare for the trip, not the rate per mile. For AI models, the trip is the finished feature, bug fix, or refactor, and the meter is every reasoning token spent getting there.

Cost per task tells a more honest story

Artificial Analysis computes exactly this: a weighted cost per task across nine hard benchmarks covering agentic work, tool use, terminal and coding tasks, science reasoning, and long-context problems, counting input, cached, reasoning, and answer tokens together. On that measure, DeepSeek V4 comes in around $0.03 per task, Kimi K3 around $0.84, GPT-5.6 Sol around $1.23, Claude Opus 5 around $2.34, and Claude Fable 5 around $3.14.

Notice what happened. The raw token gap of 100x-plus compresses to roughly 78x once you measure per completed task, because the frontier models turn out to be relatively efficient thinkers. They cost more per token but often do not ramble, so the multiple shrinks. It still does not reverse, though. DeepSeek really is dramatically cheaper per job. The lesson is the calculation itself: a low token price multiplied by a huge token count can quietly erase the savings, so you have to check whether your cheap model is also a verbose one.

You are not buying the same product

The second thing the numbers reveal is that price is not the only axis. On the Artificial Analysis Intelligence Index, DeepSeek V4 scores around 52 and Kimi K3 around 60, while Claude Opus 5 and Fable 5 lead at 62 to 63, and they lead specifically on the hardest agentic and long-horizon planning tasks. Those are exactly the jobs where AI coding gets hard: multi-step refactors, debugging across a large codebase, and long chains of tool calls where one wrong turn cascades.

So the real trade-off is this. For routine, well-scoped coding work, DeepSeek and Kimi deliver something like 80 to 95 percent of the capability at a small fraction of the cost, and they are the obvious default. For the last few points of reliability on long, tangled tasks, the premium models can still win on value rather than raw capability, because the most expensive outcome of all is a failed task you have to re-run or fix by hand. It is the classic build-versus-rework curve from engineering: the cheapest labor rate is a false economy if the defect rate is higher on work that is costly to redo.

So, does open source save you money?

Yes, for most coding workloads, and by a wide margin even after you correct for reasoning-token overhead. But the saving comes from cost-per-completed-task, not the headline token price, and it is smaller than the sticker suggests. The one caveat worth internalizing: on your hardest, longest, most failure-sensitive tasks, pay attention to success rate, not just price, because rework is the line item nobody puts on the pricing page.

A practical closing note: a single benchmark aggregate is not your workload. The genuinely rigorous move is to run your own representative coding tasks through two or three of these models and measure your own cost per completed task, since token verbosity varies a lot by task type.

Work the numbers yourself

The single most useful thing you can do is stop comparing sticker prices and compute your own cost per completed task. For one representative task, add up what you pay for the input tokens, the hidden reasoning tokens, and the answer tokens, then divide by the fraction of tasks that actually succeeded. That last division is what turns a per-token price into a per-result price. Here is a small Python snippet that does it.

def cost_per_success(p_in, p_out, in_tok, reason_tok, out_tok, success_rate):
    # prices are USD per 1M tokens; token counts are per attempted task
    spend = p_in * in_tok + p_out * (reason_tok + out_tok)
    cost_per_attempt = spend / 1_000_000
    return cost_per_attempt / success_rate  # cost per SUCCESSFUL task
# cheap-but-verbose model vs pricier-but-efficient model, same task
cheap = cost_per_success(0.14, 0.28, 4000, 20000, 1500, success_rate=0.70)
premium = cost_per_success(5.00, 25.00, 4000, 3000, 1500, success_rate=0.92)
print(f"cheap:   ${cheap:.4f} per successful task")
print(f"premium: ${premium:.4f} per successful task")

References and further reading

All pricing, cost-per-task, and Intelligence Index figures in this post come from Artificial Analysis, an independent model-benchmarking service, read at the time of writing: https://artificialanalysis.ai/models

The cost-per-task metric is derived from their Intelligence Index methodology, which aggregates nine evaluations (including GDPval, Terminal-Bench, SciCode, Humanity's Last Exam, and GPQA Diamond) and weights token costs across input, cached, reasoning, and answer tokens. Methodology details are published on the same site.

For primary-source prices, check each provider's own pricing page (DeepSeek, Moonshot AI for Kimi, OpenAI, Anthropic, and Google). Note that all figures reflect prices at the time of writing and change as providers adjust pricing, so re-check the live numbers before relying on them.