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As the Iran war stretches into its second month, gas prices continue to rise for drivers in the U.S. with no end in sight.
On Thursday, the national average price for a gallon of regular, unleaded gas hit $4.08, according to AAA’s gas tracker. That marks an increases of nearly 40% from $2.93 in the days before the U.S. and Israel launched a bombing campaign on Iran that started Feb. 28.
Experts who spoke with Money say that Americans should expect high gas prices to linger even after the Iran war concludes. And when the war will end is a big, open question.

In an address to the nation on Wednesday, President Donald Trump gave an update on the war — and, to some extent, gas prices — but did not lay out a clear time frame for a cessation of hostilities.
“Many Americans have been concerned to see the recent rise in gasoline prices here at home,” Trump said. “This short-term increase has been entirely the result of the Iranian regime.”
Trump blamed Iran for sending oil prices into disarray because it closed the Strait of Hormuz, a critical international waterway for oil trade located along the country’s southern shore. He stated that the strait will re-open once the war is over.
“When this conflict is over, the strait will open up naturally. It will just open up naturally,” Trump said. “It will resume the flowing and the gas prices will rapidly come back down.”
When Russia invaded Ukraine in February 2022, gas prices surged and peaked above $5 a gallon later that year. But the spike was relatively short-lived, and prices fell back to pre-war levels by November 2022. However, experts don’t expect gas prices to fall quickly this time around, even in the best-case scenarios.
“I don’t think it’s going to be that clear-cut,” says Patrick De Haan, head of petroleum analysis at GasBuddy. “Things could worsen before they get better.”
When will gas prices come back down?
The biggest factors driving up gas prices right now are the ongoing Iran war and the closure of the Strait of Hormuz. Although the U.S. produces most of the oil needed for gas domestically, oil is a global commodity, and major supply shocks affect its price.
For gas prices to come back down, those international issues must first be resolved.
On Wednesday, Trump said the U.S. was on track to complete all of its military objectives “very shortly,” although he suggested that the war could go on for at least “two to three weeks” as the U.S. plans to “hit [Iran] extremely hard.”
Even if the Iran war were to end today, experts say gas prices will remain elevated.
“It will take weeks, if not months, to re-open the Strait of Hormuz and for global oil production and prices to normalize,” Mark Zandi, chief economist at Moody’s Analytics, says in an email.
“There is also a lag between when oil prices decline and this results in lower gas prices,” he adds. “This goes to the old adage that gas prices go up like a rocket and fall like a feather.”
The type of fuel also makes a difference. Diesel and jet fuel, for example, have different supply chains and disruptions ripple through more slowly than gas. De Haan notes that the conflict will especially affect airlines — and the price of plane tickets — for months to come.
“I wouldn’t say you’re going to lose the summer travel season,” he says, “but it’s going to be a really, really rough one.”
‘No going back to $3 a gallon’
It’s not just a question of when gas prices might fall, but by how much. Experts tell Money that drivers shouldn’t expect to see pre-war gas prices any time soon.
“I don’t think that gasoline prices are going to return to their pre-war level because there’s going to be a heightened geopolitical risk that’s now priced into the oil market,” says Andrew Lipow, president of the energy consulting firm Lipow Oil.
Now that Iran has essentially shut down the Strait of Hormuz, Lipow says that will always be a tangible risk going forward. Basically, the cat is out of the bag.
Zandi and De Haan agree, saying that the potential for the strait shutting down in the future will make oil shipping more expensive, thus keeping gas prices elevated long-term.
“Even if the war ended today,” Zandi says, “there is no going back to $3 for a gallon of regular unleaded for the foreseeable future.”

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TheStreet aims to feature only the best products and services. If you buy something via one of our links, we may earn a commission.Why we love this dealGetting a new patio set can make your backyard feel cozy and ready for guests, but many people forget about the storage aspect during the rainy seasons and snowy weather. Having an easy and convenient way to store cushions out of the rain, or to keep your other outdoor items clean and dry — like gardening tools, children’s play toys, and more — can make life easier and prevent mold, mildew, and damage.Target has a waterproof and easy-to-assemble Costway 30-Gallon Deck Box that offers space for storage without taking up too much space. Shoppers can get it for 50% off right now at Target, paying just $40 to keep items secure and dry.Costway 30-Gallon Deck Box, $40 (was $80) at Target

Courtesy of Target

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With the price of jet fuel continuing to surge amid the war in Iran, multiple aviation insiders have rung alarm bells about the impact this will have on the aviation industry and, subsequently, air travel prices.This week, oil prices across the U.S. reached an average of $4 per gallon which is a high unseen since 2022. Prices for ready-to-use jet fuel averaged $195 a barrel. As a result, JetBlue Airways became the first U.S. airline to implement a price hike on checked baggage in connection to the rising cost of fuel while Delta Air Lines CEO Ed Bastian had previously said that an increase in oil prices of just one cent per gallon will lead to more than $40 million in annual losses for the airline.United head Scott Kirby had earlier in the month also warned that the the impact on passengers as airlines work to pass on their losses through higher prices “will probably start quick.””No assurances into June and July,” Ryanair CEO says of oil crisisThe latest airline executive to warn of the rising cost of jet fuel is Ryanair CEO Michael O’Leary. The Dublin-based budget carrier is the largest airline in Europe by total passengers carried and runs on a business model of offering rock-bottom prices on short flights between nearby European cities and then tacking on extra fees for things like baggage and seat selection.”Nobody is willing to give us any assurances into June or July,” O’Leary said of the impact on rising oil prices in an interview for The Guardian. “But if there’s a risk to 10% or 20% of the fuel supply in June, July or August, then we and all other airlines would have to start looking at cancelling some flights or taking some capacity out.”Related: Fairmont hotel in Dubai set aflame after Iranian strikeO’Leary went on to say that, out of all the countries in Europe, the United Kingdom will be hit the hardest by rising oil prices due to its reliance on oil from Kuwait (Iranian air strikes have targeted a number of key infrastructure sites over the last month).

Michael O’Leary has become the latest to sound the alarm on uncertainty around jet fuel prices.Shutterstock

“You have still got to ship it to Europe and we don’t know when or how that happens””Of all the European countries at the moment, the one that is most vulnerable is the UK because of the market share that the Kuwaitis have here,” O’Leary said further. “There could be a surplus of jet A-1 fuel in the Middle East, but you have still got to ship it to Europe and we don’t know when or how that happens.”More Travel News:Airline to launch unusual new flight to Cayman Islands from the U.S.Iranian strike hits major airport, injuries reportedUnexpected country is most luxurious travel destination for 2026U.S. government issues sudden warning on Switzerland travelContrary to U.S. airlines which have largely stopped the practice decades ago due to the risks of losses should prices change, Ryanair has hedged 80% of its fuel costs until March 2027 at $67 a barrel. Hedging means locking in prices using futures contacts.Even so, the prolonged war has brought major market uncertainty and a situation in which, according to O’Leary, the airline is “never in control of pricing.” He did not comment on any potential price hikes but refused to rule out the possibility when pressed on the subject.Related: Another cruise line cancels all sailings amid global conflict

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For the past two years, enterprises evaluating open-weight models have faced an awkward trade-off. Google’s Gemma line consistently delivered strong performance, but its custom license — with usage restrictions and terms Google could update at will — pushed many teams toward Mistral or Alibaba’s Qwen instead. Legal review added friction. Compliance teams flagged edge cases. And capable as Gemma 3 was, “open” with asterisks isn’t the same as open.Gemma 4 eliminates that friction entirely. Google DeepMind’s newest open model family ships under a standard Apache 2.0 license — the same permissive terms used by Qwen, Mistral, Arcee, and most of the open-weight ecosystem. No custom clauses, no “Harmful Use” carve-outs that required legal interpretation, no restrictions on redistribution or commercial deployment. For enterprise teams that had been waiting for Google to play on the same licensing terms as the rest of the field, the wait is over.The timing is notable. As some Chinese AI labs (most notably Alibaba’s latest Qwen models, Qwen3.5 Omni and Qwen 3.6 Plus) have begun pulling back from fully open releases for their latest models, Google is moving in the opposite direction — opening up its most capable Gemma release yet while explicitly stating the architecture draws from its commercial Gemini 3 research.Four models, two tiers: Edge to workstation in a single familyGemma 4 arrives as four distinct models organized into two deployment tiers. The “workstation” tier includes a 31B-parameter dense model and a 26B A4B Mixture-of-Experts model — both supporting text and image input with 256K-token context windows. The “edge” tier consists of the E2B and E4B, compact models designed for phones, embedded devices, and laptops, supporting text, image, and audio with 128K-token context windows.The naming convention takes some unpacking. The “E” prefix denotes “effective parameters” — the E2B has 2.3 billion effective parameters but 5.1 billion total, because each decoder layer carries its own small embedding table through a technique Google calls Per-Layer Embeddings (PLE). These tables are large on disk but cheap to compute, which is why the model runs like a 2B while technically weighing more. The “A” in 26B A4B stands for “active parameters” — only 3.8 billion of the MoE model’s 25.2 billion total parameters activate during inference, meaning it delivers roughly 26B-class intelligence with compute costs comparable to a 4B model.For IT leaders sizing GPU requirements, this translates directly to deployment flexibility. The MoE model can run on consumer-grade GPUs and should appear quickly in tools like Ollama and LM Studio. The 31B dense model requires more headroom — think an NVIDIA H100 or RTX 6000 Pro for unquantized inference — but Google is also shipping Quantization-Aware Training (QAT) checkpoints to maintain quality at lower precision. On Google Cloud, both workstation models can now run in a fully serverless configuration via Cloud Run with NVIDIA RTX Pro 6000 GPUs, spinning down to zero when idle.The MoE bet: 128 small experts to save on inference costsThe architectural choices inside the 26B A4B model deserve particular attention from teams evaluating inference economics. Rather than following the pattern of recent large MoE models that use a handful of big experts, Google went with 128 small experts, activating eight per token plus one shared always-on expert. The result is a model that benchmarks competitively with dense models in the 27B–31B range while running at roughly the speed of a 4B model during inference.This is not just a benchmark curiosity — it directly affects serving costs. A model that delivers 27B-class reasoning at 4B-class throughput means fewer GPUs, lower latency, and cheaper per-token inference in production. For organizations running coding assistants, document processing pipelines, or multi-turn agentic workflows, the MoE variant may be the most practical choice in the family.Both workstation models use a hybrid attention mechanism that interleaves local sliding window attention with full global attention, with the final layer always global. This design enables the 256K context window while keeping memory consumption manageable — an important consideration for teams processing long documents, codebases, or multi-turn agent conversations.Native multimodality: Vision, audio, and function calling baked in from scratchPrevious generations of open models typically treated multimodality as an add-on. Vision encoders were bolted onto text backbones. Audio required an external ASR pipeline like Whisper. Function calling relied on prompt engineering and hoping the model cooperated. Gemma 4 integrates all of these capabilities at the architecture level.All four models handle variable aspect-ratio image input with configurable visual token budgets — a meaningful improvement over Gemma 3n’s older vision encoder, which struggled with OCR and document understanding. The new encoder supports budgets from 70 to 1,120 tokens per image, letting developers trade off detail against compute depending on the task. Lower budgets work for classification and captioning; higher budgets handle OCR, document parsing, and fine-grained visual analysis. Multi-image and video input (processed as frame sequences) are supported natively, enabling visual reasoning across multiple documents or screenshots.The two edge models add native audio processing — automatic speech recognition and speech-to-translated-text, all on-device. The audio encoder has been compressed to 305 million parameters, down from 681 million in Gemma 3n, while the frame duration dropped from 160ms to 40ms for more responsive transcription. For teams building voice-first applications that need to keep data local — think healthcare, field service, or multilingual customer interaction — running ASR, translation, reasoning, and function calling in a single model on a phone or edge device is a genuine architectural simplification.Function calling is also native across all four models, drawing on research from Google’s FunctionGemma release late last year. Unlike previous approaches that relied on instruction-following to coax models into structured tool use, Gemma 4’s function calling was trained into the model from the ground up — optimized for multi-turn agentic flows with multiple tools. This shows up in agentic benchmarks, but more importantly, it reduces the prompt engineering overhead that enterprise teams typically invest when building tool-using agents.Benchmarks in context: Where Gemma 4 lands in a crowded fieldThe benchmark numbers tell a clear story of generational improvement. The 31B dense model scores 89.2% on AIME 2026 (a rigorous mathematical reasoning test), 80.0% on LiveCodeBench v6, and hits a Codeforces ELO of 2,150 — numbers that would have been frontier-class from proprietary models not long ago. On vision, MMMU Pro reaches 76.9% and MATH-Vision hits 85.6%. For comparison, Gemma 3 27B scored 20.8% on AIME and 29.1% on LiveCodeBench without thinking mode.The MoE model tracks closely: 88.3% on AIME 2026, 77.1% on LiveCodeBench, and 82.3% on GPQA Diamond — a graduate-level science reasoning benchmark. The performance gap between the MoE and dense variants is modest given the significant inference cost advantage of the MoE architecture.The edge models punch above their weight class. The E4B hits 42.5% on AIME 2026 and 52.0% on LiveCodeBench — strong for a model that runs on a T4 GPU. The E2B, smaller still, manages 37.5% and 44.0% respectively. Both significantly outperform Gemma 3 27B (without thinking) on most benchmarks despite being a fraction of the size, thanks to the built-in reasoning capability.These numbers need to be read against an increasingly competitive open-weight landscape. Qwen 3.5, GLM-5, and Kimi K2.5 all compete aggressively in this parameter range, and the field moves fast. What distinguishes Gemma 4 is less any single benchmark and more the combination: strong reasoning, native multimodality across text, vision, and audio, function calling, 256K context, and a genuinely permissive license — all in a single model family with deployment options from edge devices to cloud serverless.What enterprise teams should watch nextGoogle is releasing both pre-trained base models and instruction-tuned variants, which matters for organizations planning to fine-tune for specific domains. The Gemma base models have historically been strong foundations for custom training, and the Apache 2.0 license now removes any ambiguity about whether fine-tuned derivatives can be deployed commercially.The serverless deployment option via Cloud Run with GPU support is worth watching for teams that need inference capacity that scales to zero. Paying only for actual compute during inference — rather than maintaining always-on GPU instances — could meaningfully change the economics of deploying open models in production, particularly for internal tools and lower-traffic applications.Google has hinted that this may not be the complete Gemma 4 family, with additional model sizes likely to follow. But the combination available today — workstation-class reasoning models and edge-class multimodal models, all under Apache 2.0, all drawing from Gemini 3 research — represents the most complete open model release Google has shipped. For enterprise teams that had been waiting for Google’s open models to compete on licensing terms as well as performance, the evaluation can finally begin without a call to legal first.

How China will likely play a key role in restoring flows to the Strait of Hormuz and potentially avoiding a global economic recession.